#aiawareness — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #aiawareness, aggregated by home.social.
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DATE: August 24, 2026 at 11:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Study finds AI chatbots can conduct basic therapy sessions but struggle to adapt to individual needs
A recent study published in the journal Computers in Human Behavior: Artificial Humans suggests that artificial intelligence chatbots can conduct basic therapeutic conversations, though they struggle to consistently apply specific treatment techniques. The research indicates that a language model performed similarly to human practitioners in high-quality research settings, but its ability to adapt specialized psychological methods to individual needs remains inconsistent.
The global shortage of accessible mental health care has led experts to consider technology as a possible bridge. According to global health estimates cited by the study authors, hundreds of millions of individuals live with mental health conditions, yet structural barriers like cost and geographic distance leave the vast majority without professional care.
For example, the authors of a 2024 framework published in npj Mental Health Research argued that large language models hold tremendous potential to expand access to personalized treatments. A large language model (also known as an LLM) is a type of artificial intelligence trained on vast amounts of text, allowing it to generate human-like responses to prompts.
The authors of the 2024 paper proposed a roadmap for integrating these systems into clinical care, noting that psychotherapy is a high-stakes environment requiring nuanced expertise and responsible development. Despite this optimism, the actual clinical skills of these language models have remained largely untested in real-world scenarios. Previous research mostly relied on giving chatbots brief, fictional scenarios and asking human judges to rate the isolated responses for empathy or helpfulness.
These brief tests provide little insight into whether a computer program can manage a full, goal-directed therapy session that requires a coherent strategy. In a real session, a practitioner must dynamically adjust to the user’s changing emotional state and guide the conversation toward a beneficial outcome. This gap in knowledge motivated the authors of the new study to evaluate how well a language model actually performs when conducting a live, uninterrupted therapy session with a human user.
“Previous research had mostly examined LLM-chatbots’ skill at doing therapy by having them generate brief therapeutic responses or short excerpts of sessions, often in reply to a narrow set of client problems,” said study author Arthur Bran Herbener, a researcher at the Department of Psychology and Behavioral Sciences at Aarhus University. “We needed research that examines how skillfully LLM-chatbots can carry out full cognitive behavioral therapy sessions and adapt treatment to different individuals, using well-established, standardized metrics of therapeutic competence.”
To explore this question, the researchers designed an observational study involving 65 university students. These participants were experiencing mild to moderate psychological distress, such as presentation anxiety, occasional worrying, or perfectionism. Students with severe distress or diagnosed mental health disorders were excluded to ensure participant safety, as language models can sometimes generate unpredictable or inappropriate responses. Each participant completed a single 30-minute in-person session with a locally hosted artificial intelligence chatbot, exchanging an average of 49 messages.
The chatbot was programmed to deliver Cognitive Behavioral Therapy, a widely used, problem-oriented treatment that helps individuals identify and change unhelpful thoughts and behaviors. The researchers configured a specific language model to progress through the typical phases of a session. Rather than giving the chatbot a static set of rules, the researchers used a secondary background model to monitor the ongoing conversation. This secondary model evaluated the elapsed time and the current context, guiding the primary chatbot to shift from establishing a connection to conceptualizing the problem, applying an intervention, and finally wrapping up the conversation politely.
After the sessions concluded, trained graduate students read the conversation transcripts and rated the chatbot’s performance using the Cognitive Therapy Scale. This standardized assessment tool measures two main areas of clinical proficiency. The first area covers general therapeutic skills, such as expressing warmth and understanding. The second area covers specific skills, such as applying targeted techniques and guiding the user toward new insights.
To provide a benchmark for the chatbot’s scores, the researchers also conducted a meta-analysis of 18 prior studies that used the exact same scale to evaluate human practitioners. A meta-analysis is a statistical technique that combines the data from multiple independent studies to find an overall average or trend. This technique allowed the researchers to compare the chatbot’s performance to an established baseline of human competence.
The researchers found that the chatbot’s overall competence score fell slightly below the generally accepted threshold for adequate clinical performance. The standard rating scale defines an acceptable level of competence as a score of 40 out of a possible 66 points. The chatbot achieved this minimum threshold in 30 of the 65 sessions, showing a high degree of variability from one conversation to the next.
“We were surprised by how much variation the LLM-chatbot showed in its skillfulness across CBT sessions,” Herbener told PsyPost. “This is an important observation, as it suggests that we need research to ensure consistently competent care across individuals, and to understand when and why performance dips.”
When compared to the broad pool of human practitioners from the meta-analysis, the chatbot scored somewhat lower overall. The human professionals scored an average of 40.3 points on the rating scale, compared to the chatbot’s adjusted score of 38.1 points. However, the performance gap disappeared when the researchers looked only at the most rigorously conducted human studies. Compared to the six human studies judged to be of high methodological quality, the chatbot’s scores exhibited no statistically significant difference.
A closer look at the types of skills displayed by the chatbot highlighted a distinct pattern in its capabilities. The artificial intelligence performed better than human practitioners in general therapeutic skills, such as communicating empathy, validating the user’s feelings, and fostering a collaborative environment. In contrast, the chatbot struggled with the specific, technical skills required for this highly structured type of therapy. It had difficulty reliably identifying key beliefs, guiding the user through self-discovery, and adapting intervention strategies to fit the unique characteristics of each participant.
These technical struggles highlight the difference between following a predetermined structure and tailoring a method to a specific person. “LLM-chatbots designed to deliver therapy show promise in adhering to the cognitive behavioral treatment protocol, but there may be challenges in adapting to different individuals,” Herbener explained. “It’s also worth noting that good observable skill in delivering therapy is not the same as clinical effectiveness.”
“Effectiveness likely depends on factors beyond observable skill, such as positive expectations and a strong therapeutic relationship,” Herbener continued. “More comprehensive assessments of LLM-chatbots’ therapeutic competence may also require entirely new assessment approaches that account for LLMs’ distinct behavioral tendencies — such as sycophancy, or a tendency to excessively affirm users. Understanding this is crucial, since the ability to challenge clients’ beliefs is often considered important for fostering clinical change.”
These findings do not indicate that artificial intelligence is ready to replace human practitioners. The study evaluated the chatbot based on a single session with young adults experiencing only mild distress. Clinical populations often present more complex challenges, including severe hopelessness or safety risks. These complex cases place much higher demands on a practitioner’s ability to adapt and respond skillfully to unpredictability.
A single session also cannot capture the long-term planning, homework review, and relationship-building required in a complete, multi-week treatment program. In addition, the study faced challenges in consistently rating the chatbot’s text-based transcripts. The standard rating scale was originally designed for video or audio recordings, where raters can hear tone of voice and observe body language. Applying this tool to text may have made certain interpersonal skills harder to judge accurately.
Raters also knew they were evaluating an artificial intelligence, which may have influenced their scoring. In real-world applications, this lack of blinding reflects how users will actually interact with known computer systems, but it complicates direct comparisons to human professionals.
The researchers also caution against drawing overly broad conclusions from the matched scores between the chatbot and high-quality human studies. “Showing similar competence levels in CBT between human therapists and an LLM-chatbot does not mean they are equally effective ‘therapists’, nor that they are equally competent in an absolute sense,” Herbener said.
Because the human data came from past studies rather than a side-by-side test, the comparison remains indirect. “We did not directly compare the chatbot and human therapists in an experimental setting, so several factors beyond competence could bias our measurements,” Herbener added. “For example, the severity and type of psychological problems presented, or the norms for inferring behavioral observations clinicians and researchers apply when using the competence scale we relied on.”
To advance the field, future research will need to examine how chatbots perform across multiple sessions with clinical populations and investigate exactly why they sometimes fail to apply specific therapeutic techniques. “Research on LLM-chatbots in mental healthcare is still at a very early stage,” Herbener said. “Alongside design efforts aimed at ensuring consistently competent care, I’m also working to better understand the therapeutic relationship in LLM-based mental healthcare.”
Even if a chatbot can say all the right things, a user’s awareness that they are talking to a machine might alter the impact of those words. “We not only need to know how well these systems mimic human therapists’ language. We also need to understand what meaning clients attribute to that language once they know it comes from a machine,” Herbener explained. “Does the positive regard expressed by a chatbot serve the same clinical function as when it comes from a human? That’s one of the big open questions in the field.”
The study, “Exploring the therapeutic competencies of large language models: Observational study and comparison with meta-analytical estimates for human therapists,” was authored by Arthur Bran Herbener, Robert Zachariae, Michal Klincewicz, Mikkel Berg Thøgersen, Marie Rosenkrantz Hermann, and Malene Flensborg Damholdt.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AIltherapy #MentalHealthTech #CBTChatbot #TherapyTech #LLMchatbots #DigitalTherapy #MentalHealthAccess #TherapeuticCompetence #HumanVsAI #AIAwareness
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DATE: August 24, 2026 at 11:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Study finds AI chatbots can conduct basic therapy sessions but struggle to adapt to individual needs
A recent study published in the journal Computers in Human Behavior: Artificial Humans suggests that artificial intelligence chatbots can conduct basic therapeutic conversations, though they struggle to consistently apply specific treatment techniques. The research indicates that a language model performed similarly to human practitioners in high-quality research settings, but its ability to adapt specialized psychological methods to individual needs remains inconsistent.
The global shortage of accessible mental health care has led experts to consider technology as a possible bridge. According to global health estimates cited by the study authors, hundreds of millions of individuals live with mental health conditions, yet structural barriers like cost and geographic distance leave the vast majority without professional care.
For example, the authors of a 2024 framework published in npj Mental Health Research argued that large language models hold tremendous potential to expand access to personalized treatments. A large language model (also known as an LLM) is a type of artificial intelligence trained on vast amounts of text, allowing it to generate human-like responses to prompts.
The authors of the 2024 paper proposed a roadmap for integrating these systems into clinical care, noting that psychotherapy is a high-stakes environment requiring nuanced expertise and responsible development. Despite this optimism, the actual clinical skills of these language models have remained largely untested in real-world scenarios. Previous research mostly relied on giving chatbots brief, fictional scenarios and asking human judges to rate the isolated responses for empathy or helpfulness.
These brief tests provide little insight into whether a computer program can manage a full, goal-directed therapy session that requires a coherent strategy. In a real session, a practitioner must dynamically adjust to the user’s changing emotional state and guide the conversation toward a beneficial outcome. This gap in knowledge motivated the authors of the new study to evaluate how well a language model actually performs when conducting a live, uninterrupted therapy session with a human user.
“Previous research had mostly examined LLM-chatbots’ skill at doing therapy by having them generate brief therapeutic responses or short excerpts of sessions, often in reply to a narrow set of client problems,” said study author Arthur Bran Herbener, a researcher at the Department of Psychology and Behavioral Sciences at Aarhus University. “We needed research that examines how skillfully LLM-chatbots can carry out full cognitive behavioral therapy sessions and adapt treatment to different individuals, using well-established, standardized metrics of therapeutic competence.”
To explore this question, the researchers designed an observational study involving 65 university students. These participants were experiencing mild to moderate psychological distress, such as presentation anxiety, occasional worrying, or perfectionism. Students with severe distress or diagnosed mental health disorders were excluded to ensure participant safety, as language models can sometimes generate unpredictable or inappropriate responses. Each participant completed a single 30-minute in-person session with a locally hosted artificial intelligence chatbot, exchanging an average of 49 messages.
The chatbot was programmed to deliver Cognitive Behavioral Therapy, a widely used, problem-oriented treatment that helps individuals identify and change unhelpful thoughts and behaviors. The researchers configured a specific language model to progress through the typical phases of a session. Rather than giving the chatbot a static set of rules, the researchers used a secondary background model to monitor the ongoing conversation. This secondary model evaluated the elapsed time and the current context, guiding the primary chatbot to shift from establishing a connection to conceptualizing the problem, applying an intervention, and finally wrapping up the conversation politely.
After the sessions concluded, trained graduate students read the conversation transcripts and rated the chatbot’s performance using the Cognitive Therapy Scale. This standardized assessment tool measures two main areas of clinical proficiency. The first area covers general therapeutic skills, such as expressing warmth and understanding. The second area covers specific skills, such as applying targeted techniques and guiding the user toward new insights.
To provide a benchmark for the chatbot’s scores, the researchers also conducted a meta-analysis of 18 prior studies that used the exact same scale to evaluate human practitioners. A meta-analysis is a statistical technique that combines the data from multiple independent studies to find an overall average or trend. This technique allowed the researchers to compare the chatbot’s performance to an established baseline of human competence.
The researchers found that the chatbot’s overall competence score fell slightly below the generally accepted threshold for adequate clinical performance. The standard rating scale defines an acceptable level of competence as a score of 40 out of a possible 66 points. The chatbot achieved this minimum threshold in 30 of the 65 sessions, showing a high degree of variability from one conversation to the next.
“We were surprised by how much variation the LLM-chatbot showed in its skillfulness across CBT sessions,” Herbener told PsyPost. “This is an important observation, as it suggests that we need research to ensure consistently competent care across individuals, and to understand when and why performance dips.”
When compared to the broad pool of human practitioners from the meta-analysis, the chatbot scored somewhat lower overall. The human professionals scored an average of 40.3 points on the rating scale, compared to the chatbot’s adjusted score of 38.1 points. However, the performance gap disappeared when the researchers looked only at the most rigorously conducted human studies. Compared to the six human studies judged to be of high methodological quality, the chatbot’s scores exhibited no statistically significant difference.
A closer look at the types of skills displayed by the chatbot highlighted a distinct pattern in its capabilities. The artificial intelligence performed better than human practitioners in general therapeutic skills, such as communicating empathy, validating the user’s feelings, and fostering a collaborative environment. In contrast, the chatbot struggled with the specific, technical skills required for this highly structured type of therapy. It had difficulty reliably identifying key beliefs, guiding the user through self-discovery, and adapting intervention strategies to fit the unique characteristics of each participant.
These technical struggles highlight the difference between following a predetermined structure and tailoring a method to a specific person. “LLM-chatbots designed to deliver therapy show promise in adhering to the cognitive behavioral treatment protocol, but there may be challenges in adapting to different individuals,” Herbener explained. “It’s also worth noting that good observable skill in delivering therapy is not the same as clinical effectiveness.”
“Effectiveness likely depends on factors beyond observable skill, such as positive expectations and a strong therapeutic relationship,” Herbener continued. “More comprehensive assessments of LLM-chatbots’ therapeutic competence may also require entirely new assessment approaches that account for LLMs’ distinct behavioral tendencies — such as sycophancy, or a tendency to excessively affirm users. Understanding this is crucial, since the ability to challenge clients’ beliefs is often considered important for fostering clinical change.”
These findings do not indicate that artificial intelligence is ready to replace human practitioners. The study evaluated the chatbot based on a single session with young adults experiencing only mild distress. Clinical populations often present more complex challenges, including severe hopelessness or safety risks. These complex cases place much higher demands on a practitioner’s ability to adapt and respond skillfully to unpredictability.
A single session also cannot capture the long-term planning, homework review, and relationship-building required in a complete, multi-week treatment program. In addition, the study faced challenges in consistently rating the chatbot’s text-based transcripts. The standard rating scale was originally designed for video or audio recordings, where raters can hear tone of voice and observe body language. Applying this tool to text may have made certain interpersonal skills harder to judge accurately.
Raters also knew they were evaluating an artificial intelligence, which may have influenced their scoring. In real-world applications, this lack of blinding reflects how users will actually interact with known computer systems, but it complicates direct comparisons to human professionals.
The researchers also caution against drawing overly broad conclusions from the matched scores between the chatbot and high-quality human studies. “Showing similar competence levels in CBT between human therapists and an LLM-chatbot does not mean they are equally effective ‘therapists’, nor that they are equally competent in an absolute sense,” Herbener said.
Because the human data came from past studies rather than a side-by-side test, the comparison remains indirect. “We did not directly compare the chatbot and human therapists in an experimental setting, so several factors beyond competence could bias our measurements,” Herbener added. “For example, the severity and type of psychological problems presented, or the norms for inferring behavioral observations clinicians and researchers apply when using the competence scale we relied on.”
To advance the field, future research will need to examine how chatbots perform across multiple sessions with clinical populations and investigate exactly why they sometimes fail to apply specific therapeutic techniques. “Research on LLM-chatbots in mental healthcare is still at a very early stage,” Herbener said. “Alongside design efforts aimed at ensuring consistently competent care, I’m also working to better understand the therapeutic relationship in LLM-based mental healthcare.”
Even if a chatbot can say all the right things, a user’s awareness that they are talking to a machine might alter the impact of those words. “We not only need to know how well these systems mimic human therapists’ language. We also need to understand what meaning clients attribute to that language once they know it comes from a machine,” Herbener explained. “Does the positive regard expressed by a chatbot serve the same clinical function as when it comes from a human? That’s one of the big open questions in the field.”
The study, “Exploring the therapeutic competencies of large language models: Observational study and comparison with meta-analytical estimates for human therapists,” was authored by Arthur Bran Herbener, Robert Zachariae, Michal Klincewicz, Mikkel Berg Thøgersen, Marie Rosenkrantz Hermann, and Malene Flensborg Damholdt.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AIltherapy #MentalHealthTech #CBTChatbot #TherapyTech #LLMchatbots #DigitalTherapy #MentalHealthAccess #TherapeuticCompetence #HumanVsAI #AIAwareness
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DATE: August 24, 2026 at 11:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: Study finds AI chatbots can conduct basic therapy sessions but struggle to adapt to individual needs
A recent study published in the journal Computers in Human Behavior: Artificial Humans suggests that artificial intelligence chatbots can conduct basic therapeutic conversations, though they struggle to consistently apply specific treatment techniques. The research indicates that a language model performed similarly to human practitioners in high-quality research settings, but its ability to adapt specialized psychological methods to individual needs remains inconsistent.
The global shortage of accessible mental health care has led experts to consider technology as a possible bridge. According to global health estimates cited by the study authors, hundreds of millions of individuals live with mental health conditions, yet structural barriers like cost and geographic distance leave the vast majority without professional care.
For example, the authors of a 2024 framework published in npj Mental Health Research argued that large language models hold tremendous potential to expand access to personalized treatments. A large language model (also known as an LLM) is a type of artificial intelligence trained on vast amounts of text, allowing it to generate human-like responses to prompts.
The authors of the 2024 paper proposed a roadmap for integrating these systems into clinical care, noting that psychotherapy is a high-stakes environment requiring nuanced expertise and responsible development. Despite this optimism, the actual clinical skills of these language models have remained largely untested in real-world scenarios. Previous research mostly relied on giving chatbots brief, fictional scenarios and asking human judges to rate the isolated responses for empathy or helpfulness.
These brief tests provide little insight into whether a computer program can manage a full, goal-directed therapy session that requires a coherent strategy. In a real session, a practitioner must dynamically adjust to the user’s changing emotional state and guide the conversation toward a beneficial outcome. This gap in knowledge motivated the authors of the new study to evaluate how well a language model actually performs when conducting a live, uninterrupted therapy session with a human user.
“Previous research had mostly examined LLM-chatbots’ skill at doing therapy by having them generate brief therapeutic responses or short excerpts of sessions, often in reply to a narrow set of client problems,” said study author Arthur Bran Herbener, a researcher at the Department of Psychology and Behavioral Sciences at Aarhus University. “We needed research that examines how skillfully LLM-chatbots can carry out full cognitive behavioral therapy sessions and adapt treatment to different individuals, using well-established, standardized metrics of therapeutic competence.”
To explore this question, the researchers designed an observational study involving 65 university students. These participants were experiencing mild to moderate psychological distress, such as presentation anxiety, occasional worrying, or perfectionism. Students with severe distress or diagnosed mental health disorders were excluded to ensure participant safety, as language models can sometimes generate unpredictable or inappropriate responses. Each participant completed a single 30-minute in-person session with a locally hosted artificial intelligence chatbot, exchanging an average of 49 messages.
The chatbot was programmed to deliver Cognitive Behavioral Therapy, a widely used, problem-oriented treatment that helps individuals identify and change unhelpful thoughts and behaviors. The researchers configured a specific language model to progress through the typical phases of a session. Rather than giving the chatbot a static set of rules, the researchers used a secondary background model to monitor the ongoing conversation. This secondary model evaluated the elapsed time and the current context, guiding the primary chatbot to shift from establishing a connection to conceptualizing the problem, applying an intervention, and finally wrapping up the conversation politely.
After the sessions concluded, trained graduate students read the conversation transcripts and rated the chatbot’s performance using the Cognitive Therapy Scale. This standardized assessment tool measures two main areas of clinical proficiency. The first area covers general therapeutic skills, such as expressing warmth and understanding. The second area covers specific skills, such as applying targeted techniques and guiding the user toward new insights.
To provide a benchmark for the chatbot’s scores, the researchers also conducted a meta-analysis of 18 prior studies that used the exact same scale to evaluate human practitioners. A meta-analysis is a statistical technique that combines the data from multiple independent studies to find an overall average or trend. This technique allowed the researchers to compare the chatbot’s performance to an established baseline of human competence.
The researchers found that the chatbot’s overall competence score fell slightly below the generally accepted threshold for adequate clinical performance. The standard rating scale defines an acceptable level of competence as a score of 40 out of a possible 66 points. The chatbot achieved this minimum threshold in 30 of the 65 sessions, showing a high degree of variability from one conversation to the next.
“We were surprised by how much variation the LLM-chatbot showed in its skillfulness across CBT sessions,” Herbener told PsyPost. “This is an important observation, as it suggests that we need research to ensure consistently competent care across individuals, and to understand when and why performance dips.”
When compared to the broad pool of human practitioners from the meta-analysis, the chatbot scored somewhat lower overall. The human professionals scored an average of 40.3 points on the rating scale, compared to the chatbot’s adjusted score of 38.1 points. However, the performance gap disappeared when the researchers looked only at the most rigorously conducted human studies. Compared to the six human studies judged to be of high methodological quality, the chatbot’s scores exhibited no statistically significant difference.
A closer look at the types of skills displayed by the chatbot highlighted a distinct pattern in its capabilities. The artificial intelligence performed better than human practitioners in general therapeutic skills, such as communicating empathy, validating the user’s feelings, and fostering a collaborative environment. In contrast, the chatbot struggled with the specific, technical skills required for this highly structured type of therapy. It had difficulty reliably identifying key beliefs, guiding the user through self-discovery, and adapting intervention strategies to fit the unique characteristics of each participant.
These technical struggles highlight the difference between following a predetermined structure and tailoring a method to a specific person. “LLM-chatbots designed to deliver therapy show promise in adhering to the cognitive behavioral treatment protocol, but there may be challenges in adapting to different individuals,” Herbener explained. “It’s also worth noting that good observable skill in delivering therapy is not the same as clinical effectiveness.”
“Effectiveness likely depends on factors beyond observable skill, such as positive expectations and a strong therapeutic relationship,” Herbener continued. “More comprehensive assessments of LLM-chatbots’ therapeutic competence may also require entirely new assessment approaches that account for LLMs’ distinct behavioral tendencies — such as sycophancy, or a tendency to excessively affirm users. Understanding this is crucial, since the ability to challenge clients’ beliefs is often considered important for fostering clinical change.”
These findings do not indicate that artificial intelligence is ready to replace human practitioners. The study evaluated the chatbot based on a single session with young adults experiencing only mild distress. Clinical populations often present more complex challenges, including severe hopelessness or safety risks. These complex cases place much higher demands on a practitioner’s ability to adapt and respond skillfully to unpredictability.
A single session also cannot capture the long-term planning, homework review, and relationship-building required in a complete, multi-week treatment program. In addition, the study faced challenges in consistently rating the chatbot’s text-based transcripts. The standard rating scale was originally designed for video or audio recordings, where raters can hear tone of voice and observe body language. Applying this tool to text may have made certain interpersonal skills harder to judge accurately.
Raters also knew they were evaluating an artificial intelligence, which may have influenced their scoring. In real-world applications, this lack of blinding reflects how users will actually interact with known computer systems, but it complicates direct comparisons to human professionals.
The researchers also caution against drawing overly broad conclusions from the matched scores between the chatbot and high-quality human studies. “Showing similar competence levels in CBT between human therapists and an LLM-chatbot does not mean they are equally effective ‘therapists’, nor that they are equally competent in an absolute sense,” Herbener said.
Because the human data came from past studies rather than a side-by-side test, the comparison remains indirect. “We did not directly compare the chatbot and human therapists in an experimental setting, so several factors beyond competence could bias our measurements,” Herbener added. “For example, the severity and type of psychological problems presented, or the norms for inferring behavioral observations clinicians and researchers apply when using the competence scale we relied on.”
To advance the field, future research will need to examine how chatbots perform across multiple sessions with clinical populations and investigate exactly why they sometimes fail to apply specific therapeutic techniques. “Research on LLM-chatbots in mental healthcare is still at a very early stage,” Herbener said. “Alongside design efforts aimed at ensuring consistently competent care, I’m also working to better understand the therapeutic relationship in LLM-based mental healthcare.”
Even if a chatbot can say all the right things, a user’s awareness that they are talking to a machine might alter the impact of those words. “We not only need to know how well these systems mimic human therapists’ language. We also need to understand what meaning clients attribute to that language once they know it comes from a machine,” Herbener explained. “Does the positive regard expressed by a chatbot serve the same clinical function as when it comes from a human? That’s one of the big open questions in the field.”
The study, “Exploring the therapeutic competencies of large language models: Observational study and comparison with meta-analytical estimates for human therapists,” was authored by Arthur Bran Herbener, Robert Zachariae, Michal Klincewicz, Mikkel Berg Thøgersen, Marie Rosenkrantz Hermann, and Malene Flensborg Damholdt.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AIltherapy #MentalHealthTech #CBTChatbot #TherapyTech #LLMchatbots #DigitalTherapy #MentalHealthAccess #TherapeuticCompetence #HumanVsAI #AIAwareness
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#WorthARead Schneeberger: Intelligente Medizinprodukte: Rechtsfragen am Schnittpunkt von DSGVO, MPVO und AI Act
#AIAct #Art10KIVO #Datenschutzrecht #MDR #Medizinprodukt #aiawareness
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DATE: August 19, 2026 at 08:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: How the use of artificial intelligence harms college students’ ability to learn
College students who copy answers from artificial intelligence tools without verifying the information experience declines in their ability to learn independently. These unreflective habits are associated with a weaker belief in students’ own capabilities and a reduced motivation to learn. The findings were published in Scientific Reports.
Generative artificial intelligence programs can quickly write essays, generate computer code, and solve advanced mathematical equations. These tools have become common academic aids on college campuses across the globe. When students use these systems to seek explanations or brainstorm new ideas, the technology can expand their academic perspectives and improve their studying efficiency.
But reliance on automated answers carries potential academic risks. Hui Zhao and Huijuan Gu, researchers at Zhoukou Normal University, designed a study to examine what happens when students use artificial intelligence thoughtlessly. The researchers define thoughtless use as a pattern in which users blindly adopt machine-generated text without critically evaluating, verifying, or deeply understanding the outputs.
The researchers wanted to know how this specific habit affects self-directed learning. Self-directed learning is a core educational skill in higher education. It refers to a student’s capacity to independently set goals, apply study strategies, and monitor their own academic progress. Students with high self-directed learning abilities can manage their time and actively evaluate what they do and do not understand.
To understand the relationship between technology habits and study skills, the researchers looked at two psychological factors. The first is self-efficacy, which is a person’s internal belief in their capacity to successfully complete tasks and overcome challenges. The second factor is learning motivation, which encompasses the psychological drives that push a student to initiate and persist in their coursework. The researchers based their investigation on the idea that a student’s environment, personal beliefs, and daily behaviors all shape one another in a continuous, reciprocal cycle.
Zhao and Gu recruited 487 undergraduate students from four universities in Henan Province, China, to complete an online survey. The participants answered a series of questions assessing their study habits on a five-point scale. The respondents ranged from college freshmen to seniors and represented a variety of academic disciplines, including the humanities, social sciences, and natural sciences.
The survey measured four distinct categories using established psychological scales. First, it assessed how often students engaged in thoughtless artificial intelligence use. Participants rated their agreement with statements such as “I copy learning tasks or problem statements into generative AI to seek answers or ideas” and “I feel that generative AI is more capable than I am in learning and problem-solving.”
Second, the survey measured the students’ academic self-efficacy. This section asked participants to reflect on their confidence when facing difficult materials, using prompts like “I believe that I can independently master complex learning content.”
Third, the researchers measured the students’ overall motivation to learn. This included questions about their curiosity, their desire to master new skills, and the satisfaction they gained from overcoming academic hurdles. Finally, the survey evaluated the students’ capacity for self-directed learning by asking how often they actively checked their own understanding of the material or created knowledge frameworks like mind maps.
The researchers used a statistical technique called structural equation modeling to analyze the survey responses. This analytical method allows scientists to observe complex networks of relationships between multiple different variables at the same time, rather than just looking at two variables in isolation.
The analysis revealed that the thoughtless use of artificial intelligence is strongly associated with lower levels of self-directed learning. Students who habitually relied on automated answers reported worse self-management skills and lower cognitive engagement in their coursework.
The researchers identified a specific psychological pathway explaining this decline. The authors suggest that an unreflective reliance on technology deprives students of the opportunity to struggle through complex problems. Without the experience of overcoming academic challenges through their own hard work, students reported lower levels of self-efficacy.
This lower confidence in their own abilities was in turn linked to a reduced motivation to learn. When individuals do not believe they can succeed on their own, their willingness to invest effort naturally declines. With less motivation and lower self-confidence, the students became less capable of managing their own educational progress, acting instead as dependent users of external tools.
The researchers also conducted a multi-group analysis to see if these patterns differed between men and women. This statistical test allowed the authors to compare the strength of the psychological relationships across the two demographic groups.
The analysis uncovered distinct gender differences in how thoughtless technology use relates to psychological well-being. For male students, unreflective use showed a stronger negative association with learning motivation. The researchers suggest that male students often view technology primarily as a tool for efficiency, meaning that immediate automated answers quickly replace their internal drive to engage deeply with the material.
For female students, thoughtless use was associated with steeper declines in self-efficacy and self-directed learning. The researchers note that female students tend to adopt more reflective and evaluative study strategies in their traditional coursework. When they bypass these reflective processes by copying automated answers, they miss out on the mastery experiences that traditionally build their academic confidence.
The study relies on cross-sectional data, meaning all the information was collected at a single point in time. Because the researchers did not track the students over a long period, the statistical results highlight relationships between behaviors and beliefs but cannot definitively prove cause and effect. It is possible that students who already have low self-efficacy are simply more likely to use artificial intelligence thoughtlessly.
The demographic makeup of the sample also presents some limitations. The participants were drawn exclusively from universities in a single Chinese province, and nearly eighty percent of the respondents were female. Expanding future surveys to include a more balanced demographic distribution across different regions would help verify if these patterns hold true for other student populations.
Additionally, the survey measured motivation as a single, unified concept. Future research could split this category into intrinsic motivation, such as natural curiosity, and extrinsic motivation, such as a desire for good grades. Separating these concepts would provide a more detailed picture of how automated tools influence a student’s inner drive to succeed. Future studies might also track students over an entire semester to see how their habits and self-confidence evolve as they face different types of academic challenges.
The study, “Thoughtless Use of Generative Artificial Intelligence and College Students’ Self-Directed Learning: A Multi-Group SEM Analysis of Gender Differences,” was authored by Hui Zhao and Huijuan Gu.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AIAwareness #StudentLearning #SelfDirectedLearning #AcademicIntegrity #GenerativeAI #SelfEfficacy #LearningMotivation #EducationResearch #ThinkBeforeYouCopy #GenderDifferencesAI
-
DATE: August 19, 2026 at 08:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: How the use of artificial intelligence harms college students’ ability to learn
College students who copy answers from artificial intelligence tools without verifying the information experience declines in their ability to learn independently. These unreflective habits are associated with a weaker belief in students’ own capabilities and a reduced motivation to learn. The findings were published in Scientific Reports.
Generative artificial intelligence programs can quickly write essays, generate computer code, and solve advanced mathematical equations. These tools have become common academic aids on college campuses across the globe. When students use these systems to seek explanations or brainstorm new ideas, the technology can expand their academic perspectives and improve their studying efficiency.
But reliance on automated answers carries potential academic risks. Hui Zhao and Huijuan Gu, researchers at Zhoukou Normal University, designed a study to examine what happens when students use artificial intelligence thoughtlessly. The researchers define thoughtless use as a pattern in which users blindly adopt machine-generated text without critically evaluating, verifying, or deeply understanding the outputs.
The researchers wanted to know how this specific habit affects self-directed learning. Self-directed learning is a core educational skill in higher education. It refers to a student’s capacity to independently set goals, apply study strategies, and monitor their own academic progress. Students with high self-directed learning abilities can manage their time and actively evaluate what they do and do not understand.
To understand the relationship between technology habits and study skills, the researchers looked at two psychological factors. The first is self-efficacy, which is a person’s internal belief in their capacity to successfully complete tasks and overcome challenges. The second factor is learning motivation, which encompasses the psychological drives that push a student to initiate and persist in their coursework. The researchers based their investigation on the idea that a student’s environment, personal beliefs, and daily behaviors all shape one another in a continuous, reciprocal cycle.
Zhao and Gu recruited 487 undergraduate students from four universities in Henan Province, China, to complete an online survey. The participants answered a series of questions assessing their study habits on a five-point scale. The respondents ranged from college freshmen to seniors and represented a variety of academic disciplines, including the humanities, social sciences, and natural sciences.
The survey measured four distinct categories using established psychological scales. First, it assessed how often students engaged in thoughtless artificial intelligence use. Participants rated their agreement with statements such as “I copy learning tasks or problem statements into generative AI to seek answers or ideas” and “I feel that generative AI is more capable than I am in learning and problem-solving.”
Second, the survey measured the students’ academic self-efficacy. This section asked participants to reflect on their confidence when facing difficult materials, using prompts like “I believe that I can independently master complex learning content.”
Third, the researchers measured the students’ overall motivation to learn. This included questions about their curiosity, their desire to master new skills, and the satisfaction they gained from overcoming academic hurdles. Finally, the survey evaluated the students’ capacity for self-directed learning by asking how often they actively checked their own understanding of the material or created knowledge frameworks like mind maps.
The researchers used a statistical technique called structural equation modeling to analyze the survey responses. This analytical method allows scientists to observe complex networks of relationships between multiple different variables at the same time, rather than just looking at two variables in isolation.
The analysis revealed that the thoughtless use of artificial intelligence is strongly associated with lower levels of self-directed learning. Students who habitually relied on automated answers reported worse self-management skills and lower cognitive engagement in their coursework.
The researchers identified a specific psychological pathway explaining this decline. The authors suggest that an unreflective reliance on technology deprives students of the opportunity to struggle through complex problems. Without the experience of overcoming academic challenges through their own hard work, students reported lower levels of self-efficacy.
This lower confidence in their own abilities was in turn linked to a reduced motivation to learn. When individuals do not believe they can succeed on their own, their willingness to invest effort naturally declines. With less motivation and lower self-confidence, the students became less capable of managing their own educational progress, acting instead as dependent users of external tools.
The researchers also conducted a multi-group analysis to see if these patterns differed between men and women. This statistical test allowed the authors to compare the strength of the psychological relationships across the two demographic groups.
The analysis uncovered distinct gender differences in how thoughtless technology use relates to psychological well-being. For male students, unreflective use showed a stronger negative association with learning motivation. The researchers suggest that male students often view technology primarily as a tool for efficiency, meaning that immediate automated answers quickly replace their internal drive to engage deeply with the material.
For female students, thoughtless use was associated with steeper declines in self-efficacy and self-directed learning. The researchers note that female students tend to adopt more reflective and evaluative study strategies in their traditional coursework. When they bypass these reflective processes by copying automated answers, they miss out on the mastery experiences that traditionally build their academic confidence.
The study relies on cross-sectional data, meaning all the information was collected at a single point in time. Because the researchers did not track the students over a long period, the statistical results highlight relationships between behaviors and beliefs but cannot definitively prove cause and effect. It is possible that students who already have low self-efficacy are simply more likely to use artificial intelligence thoughtlessly.
The demographic makeup of the sample also presents some limitations. The participants were drawn exclusively from universities in a single Chinese province, and nearly eighty percent of the respondents were female. Expanding future surveys to include a more balanced demographic distribution across different regions would help verify if these patterns hold true for other student populations.
Additionally, the survey measured motivation as a single, unified concept. Future research could split this category into intrinsic motivation, such as natural curiosity, and extrinsic motivation, such as a desire for good grades. Separating these concepts would provide a more detailed picture of how automated tools influence a student’s inner drive to succeed. Future studies might also track students over an entire semester to see how their habits and self-confidence evolve as they face different types of academic challenges.
The study, “Thoughtless Use of Generative Artificial Intelligence and College Students’ Self-Directed Learning: A Multi-Group SEM Analysis of Gender Differences,” was authored by Hui Zhao and Huijuan Gu.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AIAwareness #StudentLearning #SelfDirectedLearning #AcademicIntegrity #GenerativeAI #SelfEfficacy #LearningMotivation #EducationResearch #ThinkBeforeYouCopy #GenderDifferencesAI
-
DATE: August 19, 2026 at 08:00AM
SOURCE: PSYPOST.ORG** Research quality varies widely from fantastic to small exploratory studies. Please check research methods when conclusions are very important to you. **
-------------------------------------------------TITLE: How the use of artificial intelligence harms college students’ ability to learn
College students who copy answers from artificial intelligence tools without verifying the information experience declines in their ability to learn independently. These unreflective habits are associated with a weaker belief in students’ own capabilities and a reduced motivation to learn. The findings were published in Scientific Reports.
Generative artificial intelligence programs can quickly write essays, generate computer code, and solve advanced mathematical equations. These tools have become common academic aids on college campuses across the globe. When students use these systems to seek explanations or brainstorm new ideas, the technology can expand their academic perspectives and improve their studying efficiency.
But reliance on automated answers carries potential academic risks. Hui Zhao and Huijuan Gu, researchers at Zhoukou Normal University, designed a study to examine what happens when students use artificial intelligence thoughtlessly. The researchers define thoughtless use as a pattern in which users blindly adopt machine-generated text without critically evaluating, verifying, or deeply understanding the outputs.
The researchers wanted to know how this specific habit affects self-directed learning. Self-directed learning is a core educational skill in higher education. It refers to a student’s capacity to independently set goals, apply study strategies, and monitor their own academic progress. Students with high self-directed learning abilities can manage their time and actively evaluate what they do and do not understand.
To understand the relationship between technology habits and study skills, the researchers looked at two psychological factors. The first is self-efficacy, which is a person’s internal belief in their capacity to successfully complete tasks and overcome challenges. The second factor is learning motivation, which encompasses the psychological drives that push a student to initiate and persist in their coursework. The researchers based their investigation on the idea that a student’s environment, personal beliefs, and daily behaviors all shape one another in a continuous, reciprocal cycle.
Zhao and Gu recruited 487 undergraduate students from four universities in Henan Province, China, to complete an online survey. The participants answered a series of questions assessing their study habits on a five-point scale. The respondents ranged from college freshmen to seniors and represented a variety of academic disciplines, including the humanities, social sciences, and natural sciences.
The survey measured four distinct categories using established psychological scales. First, it assessed how often students engaged in thoughtless artificial intelligence use. Participants rated their agreement with statements such as “I copy learning tasks or problem statements into generative AI to seek answers or ideas” and “I feel that generative AI is more capable than I am in learning and problem-solving.”
Second, the survey measured the students’ academic self-efficacy. This section asked participants to reflect on their confidence when facing difficult materials, using prompts like “I believe that I can independently master complex learning content.”
Third, the researchers measured the students’ overall motivation to learn. This included questions about their curiosity, their desire to master new skills, and the satisfaction they gained from overcoming academic hurdles. Finally, the survey evaluated the students’ capacity for self-directed learning by asking how often they actively checked their own understanding of the material or created knowledge frameworks like mind maps.
The researchers used a statistical technique called structural equation modeling to analyze the survey responses. This analytical method allows scientists to observe complex networks of relationships between multiple different variables at the same time, rather than just looking at two variables in isolation.
The analysis revealed that the thoughtless use of artificial intelligence is strongly associated with lower levels of self-directed learning. Students who habitually relied on automated answers reported worse self-management skills and lower cognitive engagement in their coursework.
The researchers identified a specific psychological pathway explaining this decline. The authors suggest that an unreflective reliance on technology deprives students of the opportunity to struggle through complex problems. Without the experience of overcoming academic challenges through their own hard work, students reported lower levels of self-efficacy.
This lower confidence in their own abilities was in turn linked to a reduced motivation to learn. When individuals do not believe they can succeed on their own, their willingness to invest effort naturally declines. With less motivation and lower self-confidence, the students became less capable of managing their own educational progress, acting instead as dependent users of external tools.
The researchers also conducted a multi-group analysis to see if these patterns differed between men and women. This statistical test allowed the authors to compare the strength of the psychological relationships across the two demographic groups.
The analysis uncovered distinct gender differences in how thoughtless technology use relates to psychological well-being. For male students, unreflective use showed a stronger negative association with learning motivation. The researchers suggest that male students often view technology primarily as a tool for efficiency, meaning that immediate automated answers quickly replace their internal drive to engage deeply with the material.
For female students, thoughtless use was associated with steeper declines in self-efficacy and self-directed learning. The researchers note that female students tend to adopt more reflective and evaluative study strategies in their traditional coursework. When they bypass these reflective processes by copying automated answers, they miss out on the mastery experiences that traditionally build their academic confidence.
The study relies on cross-sectional data, meaning all the information was collected at a single point in time. Because the researchers did not track the students over a long period, the statistical results highlight relationships between behaviors and beliefs but cannot definitively prove cause and effect. It is possible that students who already have low self-efficacy are simply more likely to use artificial intelligence thoughtlessly.
The demographic makeup of the sample also presents some limitations. The participants were drawn exclusively from universities in a single Chinese province, and nearly eighty percent of the respondents were female. Expanding future surveys to include a more balanced demographic distribution across different regions would help verify if these patterns hold true for other student populations.
Additionally, the survey measured motivation as a single, unified concept. Future research could split this category into intrinsic motivation, such as natural curiosity, and extrinsic motivation, such as a desire for good grades. Separating these concepts would provide a more detailed picture of how automated tools influence a student’s inner drive to succeed. Future studies might also track students over an entire semester to see how their habits and self-confidence evolve as they face different types of academic challenges.
The study, “Thoughtless Use of Generative Artificial Intelligence and College Students’ Self-Directed Learning: A Multi-Group SEM Analysis of Gender Differences,” was authored by Hui Zhao and Huijuan Gu.
-------------------------------------------------
Private, vetted email list for mental health professionals: https://www.clinicians-exchange.org
Unofficial Psychology Today Xitter to toot feed at Psych Today Unofficial Bot @PTUnofficialBot
-------------------------------------------------
#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AIAwareness #StudentLearning #SelfDirectedLearning #AcademicIntegrity #GenerativeAI #SelfEfficacy #LearningMotivation #EducationResearch #ThinkBeforeYouCopy #GenderDifferencesAI
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#WorthARead Bontcheva/Bechmann/Pedreschi/Gregorio/Riess/Botan: Code of Practice on Transparency of AI-Generated Content
https://privacyawareness.at/literature/code-of-practice-on-transparency-of-ai-generated-content
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#WorthARead Küchli/Kindermann: Geldbußen ohne Definitionen - Behörden und öffentliche Stellen im Fokus des Art 99 AI Act (Teil I)
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#Lesenswert Allhutter/Fischer/Habiger/Schönanger: Digitale Transformation der öffentlichen Verwaltung in Österreich
-
#Lesenswert Löffler: Künstliche Intelligenz in der Verwaltung von Forschungseinrichtungen
-
#WorthARead Löffler: Datenschutzrechtliche Aspekte von KI-Systemen
https://privacyawareness.at/literature/datenschutzrechtliche-aspekte-von-ki-systemen
-
#WorthARead Feldinger: Einsatzbereiche generativer KI für Rechtsanwender:innen
https://privacyawareness.at/literature/einsatzbereiche-generativer-ki-fur-rechtsanwender-innen
-
#Lesenswert Müller: Die Bedeutung von Transparenz und Erklärung beim Einsatz von KI-Agenten im Beschäftigtenkontext: Ein Annäherungsversuch
#Arbeitsrecht #Art22DSGVO #Art86KIVO #DSGVO #KIVO #XAI #aiawareness
-
#WorthARead Bontcheva/Bechmann/Pedreschi/Gregorio/Riess/Botan: Second Draft Code of Practice on Transparency of AI-Generated Content
-
#WorthARead Datenschutzbehörde: Tätigkeitsbericht 2025
https://privacyawareness.at/literature/tatigkeitsbericht-2025
-
#WorthARead Ségur-Cabanac: Artikel 86 KI-VO: Das Recht auf Erläuterung im Spannungsfeld von Transparenz und Rechtsdurchsetzung
-
AI-generated antisemitism hit 30 million views as platforms fall behind: CyberWell's report found 307 AI-generated antisemitic posts hit 30M views in 13 months. TikTok removed 88%, YouTube only 28%, and X just 20% of flagged content. https://ppc.land/ai-generated-antisemitism-hit-30-million-views-as-platforms-fall-behind/ #AIAwareness #Antisemitism #SocialMediaSafety #CyberWell #OnlineHate
-
AI-generated antisemitism hit 30 million views as platforms fall behind: CyberWell's report found 307 AI-generated antisemitic posts hit 30M views in 13 months. TikTok removed 88%, YouTube only 28%, and X just 20% of flagged content. https://ppc.land/ai-generated-antisemitism-hit-30-million-views-as-platforms-fall-behind/ #AIAwareness #Antisemitism #SocialMediaSafety #CyberWell #OnlineHate
-
AI-generated antisemitism hit 30 million views as platforms fall behind: CyberWell's report found 307 AI-generated antisemitic posts hit 30M views in 13 months. TikTok removed 88%, YouTube only 28%, and X just 20% of flagged content. https://ppc.land/ai-generated-antisemitism-hit-30-million-views-as-platforms-fall-behind/ #AIAwareness #Antisemitism #SocialMediaSafety #CyberWell #OnlineHate
-
#Lesenswert Verein zur Förderung eines Deutschen Forschungsnetzes e. V.: DFN-Infobrief Recht April
https://privacyawareness.at/literature/dfn-infobrief-recht-april
#DSGVO #DSGVOVerfahrensverordnung #KI #Urheberrecht #Wissenschaftsfreiheit #aiawareness
-
#Lesenswert Verein zur Förderung eines Deutschen Forschungsnetzes e. V.: DFN-Infobrief Recht März
https://privacyawareness.at/literature/dfn-infobrief-recht-marz
#CRA #DGA #DMA #DSA #DataAct #Datenschutz #EHDS #KI #NIS2 #aiawareness #eIDAS
-
#WorthARead d’Hauwers: Data Brokers Market Study
https://privacyawareness.at/literature/data-brokers-market-study
-
#WorthARead Almada: Law & Compliance in AI Security & Data Protection
https://privacyawareness.at/literature/law-compliance-in-ai-security-data-protection
-
#WorthARead Glerean: Fundamentals of Secure AI Systems with Personal Data
https://privacyawareness.at/literature/fundamentals-of-secure-ai-systems-with-personal-data
-
#Lesenswert Rothmund-Burgwall: Einsatz von KI als neuartige Datenverarbeitung?
https://privacyawareness.at/literature/einsatz-von-ki-als-neuartige-datenverarbeitung
#Art3Z1KIVO #Art35DSGVO #DSGVO #Dako #Datenschutzrecht #KI #KIVO #aiawareness
-
#WorthARead Datenschutzkonferenz: Orientierungshilfe zu empfohlenen technischen und organisatorischen Maßnahmen bei der Entwicklung und beim Betrieb von KI-Systemen
#Art17DSGVO #Datenschutzrecht #KI #Memorisierung #aiawareness
-
#WorthARead Lermen/Paleka/Swanson/Aerni/Carlini/Tramèr: Large-scale online deanonymization with LLMs
https://privacyawareness.at/literature/large-scale-online-deanonymization-with-llms
#Datenschutz #Deanonymisierung #KI #Vertrauenswürdigkeit #aiawareness
-
#WorthARead Datenschutzkonferenz: Orientierungshilfe zu empfohlenen technischen und organisatorischen Maßnahmen bei der Entwicklung und beim Betrieb von KI-Systemen
#Art17DSGVO #Datenschutzrecht #KI #Memorisierung #aiawareness
-
#Lesenswert Kastelitz/Löffler: "Dieses Meeting wird aufgezeichnet" - Datenschutz- und AI Act-Compliance bei Aufzeichnung und Transkription von Online-Meetings
-
#WorthARead Europäischer Datenschutzausschuss (EDSA): Stellungnahme 28/2024 zu gewissen Datenschutzaspekten der Verarbeitung personenbezogener Daten im Zusammenhang mit KI-Modellen
#DSGVO #Datenschutzrecht #Fruitofthepoisonoustree #KI #aiawareness
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#Lesenswert Specht-Riemenschneider/Bortnikov/Steffens: Keine „Fruit of the poisonous KI “-Doktrin im Datenschutzrecht
https://privacyawareness.at/literature/keine-fruit-of-the-poisonous-ki-doktrin-im-datenschutzrecht
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#Lesenswert Hense/Wagner: Gutachten „KI- Rechtsgrundlage“
https://privacyawareness.at/literature/gutachten-ki-rechtsgrundlage
-
#Lesenswert Haidinger/Löffler: Auskunftserteilung bei automatisierter Entscheidungsfindung muss präzise und verständlich sein
-
#Lesenswert Piska/Schellenbacher/Seeber: Datenschutz als Innovationsbremse: Hebelt die DSGVO Künstliche Intelligenz aus? - KI vs Datenschutz: Wer stoppt wen?
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#Lesenswert Bundesbeauftragte für den Datenschutz und die Informationsfreiheit (BfDI): KI in Behörden – Datenschutz von Anfang an mitdenken
https://privacyawareness.at/literature/ki-in-behorden-datenschutz-von-anfang-an-mitdenken
-
#Lesenswert Datenschutzkonferenz: Orientierungshilfe zu datenschutzrechtlichen Besonderheiten generativer KI-Systeme mit RAG-Methode
#Datenschutzrecht #Fruitofthepoisonoustree #KI #RAG #aiawareness
-
#WorthARead : OLG Köln 23.05.2025, 15 UKl 2/25
https://privacyawareness.at/literature/olg-koln-23-05-2025-15-ukl-2-25
-
Is AI hurting your ability to think? How to reclaim your brain
#AI #AIEthics #CognitiveAtrophy #CriticalThinking #DigitalWellbeing #HumanIntelligence #AIResponsibility #AlgorithmicBias #ThinkingSkills #MentalAutonomy #TechImpact #AIAwareness #Creativity
https://the-14.com/is-ai-hurting-your-ability-to-think-how-to-reclaim-your-brain/ -
Is AI hurting your ability to think? How to reclaim your brain
#AI #AIEthics #CognitiveAtrophy #CriticalThinking #DigitalWellbeing #HumanIntelligence #AIResponsibility #AlgorithmicBias #ThinkingSkills #MentalAutonomy #TechImpact #AIAwareness #Creativity
https://the-14.com/is-ai-hurting-your-ability-to-think-how-to-reclaim-your-brain/ -
In the Age of AI, Think Critically, Write Creatively, Act Compassionately
In the Age of AI, Think Critically, Write Creatively, Act Compassionately- How To…
https://writersfunzone.com/blog/2025/11/04/in-the-age-of-ai-think-critically-write-creatively-act-compassionately#HOWTOWRITETHEFUTUREPODCAST #AIawareness #askingquestions #BethBarany #brainscience
-
In the Age of AI, Think Critically, Write Creatively, Act Compassionately
In the Age of AI, Think Critically, Write Creatively, Act Compassionately- How To…
https://writersfunzone.com/blog/2025/11/04/in-the-age-of-ai-think-critically-write-creatively-act-compassionately#HOWTOWRITETHEFUTUREPODCAST #AIawareness #askingquestions #BethBarany #brainscience
-
In the Age of AI, Think Critically, Write Creatively, Act Compassionately
In the Age of AI, Think Critically, Write Creatively, Act Compassionately- How To…
https://writersfunzone.com/blog/2025/11/04/in-the-age-of-ai-think-critically-write-creatively-act-compassionately#HOWTOWRITETHEFUTUREPODCAST #AIawareness #askingquestions #BethBarany #brainscience
-
Pause Before You Share: The Hidden Risks of Online AI Chats
https://youtu.be/8DjVuD8XaqY #cybersecurity #privacy #DigitalPrivacy #LLM #AI #chatbots #AIEthics #ProtectYourData #PrivacyMatters #AIAwareness -
Pause Before You Share: The Hidden Risks of Online AI Chats
https://youtu.be/8DjVuD8XaqY #cybersecurity #privacy #DigitalPrivacy #LLM #AI #chatbots #AIEthics #ProtectYourData #PrivacyMatters #AIAwareness -
Pause Before You Share: The Hidden Risks of Online AI Chats
https://youtu.be/8DjVuD8XaqY #cybersecurity #privacy #DigitalPrivacy #LLM #AI #chatbots #AIEthics #ProtectYourData #PrivacyMatters #AIAwareness -
Chatbots are on the rise, but customers still trust human agents more
#Chatbots #CustomerService #AI #HumanAgents #CustomerExperience #TechTrust #AIAwareness #DigitalSupport #CustomerEngagement #HybridApproach
https://the-14.com/chatbots-are-on-the-rise-but-customers-still-trust-human-agents-more/ -
Chatbots are on the rise, but customers still trust human agents more
#Chatbots #CustomerService #AI #HumanAgents #CustomerExperience #TechTrust #AIAwareness #DigitalSupport #CustomerEngagement #HybridApproach
https://the-14.com/chatbots-are-on-the-rise-but-customers-still-trust-human-agents-more/ -
Chatbots are on the rise, but customers still trust human agents more
#Chatbots #CustomerService #AI #HumanAgents #CustomerExperience #TechTrust #AIAwareness #DigitalSupport #CustomerEngagement #HybridApproach
https://the-14.com/chatbots-are-on-the-rise-but-customers-still-trust-human-agents-more/ -
Chatbots are on the rise, but customers still trust human agents more
#Chatbots #CustomerService #AI #HumanAgents #CustomerExperience #TechTrust #AIAwareness #DigitalSupport #CustomerEngagement #HybridApproach
https://the-14.com/chatbots-are-on-the-rise-but-customers-still-trust-human-agents-more/ -
Humans, Not AI, Are the Biggest Threat to Humanity https://youtube.com/shorts/nKvEJQjrPSY?feature=share
Main Site: https://www.analyse.asia/generativeai-creativity-and-the-human-ai-for-humanity-with-tan-siok-siok/
YouTube: https://youtu.be/v_lTa3YWNIs
Newsletter Signup: https://www.analyse.asia/#/portal/signup
LinkedIn Page: https://www.linkedin.com/company/analyse-asia/#responsibleai #HumanAgency #AIForGood #AIAwareness #HumanCollaboration
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Humans, Not AI, Are the Biggest Threat to Humanity https://youtube.com/shorts/nKvEJQjrPSY?feature=share
Main Site: https://www.analyse.asia/generativeai-creativity-and-the-human-ai-for-humanity-with-tan-siok-siok/
YouTube: https://youtu.be/v_lTa3YWNIs
Newsletter Signup: https://www.analyse.asia/#/portal/signup
LinkedIn Page: https://www.linkedin.com/company/analyse-asia/#responsibleai #HumanAgency #AIForGood #AIAwareness #HumanCollaboration
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Ever felt like your #ProfessionalContent is being repurposed? Here's how to stop LinkedIn from using your posts to train AI: https://www.linkedin.com/mypreferences/d/settings/data-for-ai-improvement
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@Frankacy Absolutely agree! Just like email scams, recognizing AI-generated content will become a critical digital literacy skill. It's fascinating to think about how future generations will adapt to these new challenges. #DigitalLiteracy #AIawareness
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@Frankacy Absolutely agree! Just like email scams, recognizing AI-generated content will become a critical digital literacy skill. It's fascinating to think about how future generations will adapt to these new challenges. #DigitalLiteracy #AIawareness
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@Frankacy Absolutely agree! Just like email scams, recognizing AI-generated content will become a critical digital literacy skill. It's fascinating to think about how future generations will adapt to these new challenges. #DigitalLiteracy #AIawareness
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@Frankacy Absolutely agree! Just like email scams, recognizing AI-generated content will become a critical digital literacy skill. It's fascinating to think about how future generations will adapt to these new challenges. #DigitalLiteracy #AIawareness