#cognitive-offloading — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #cognitive-offloading, aggregated by home.social.
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DATE: July 15, 2026 at 04:00PM
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: Is AI making us stupid through cognitive offloading? New review explores the evidence
A new theoretical review published in Trends in Cognitive Sciences explores whether relying on artificial intelligence compromises human intelligence. The paper suggests that while outsourcing mental tasks to algorithms can erode specific learned skills, our foundational cognitive abilities are likely more resilient. The impact of these tools largely depends on how people choose to interact with them.
Humans regularly use external tools to reduce the mental effort required to complete tasks. This behavior is known as cognitive offloading. Common examples include using a calculator for arithmetic or relying on a satellite navigation system for directions.
The recent explosion of generative artificial intelligence has brought the concept of cognitive offloading back into the spotlight. Generative artificial intelligence refers to computer programs that can instantly create text, solve problems, or analyze data based on massive amounts of existing information. Because these platforms can synthesize complex ideas in seconds, many worry that human intellect will suffer.
Trent N. Cash, Megan O. Kelly, Brooke N. Macnamara, and Evan F. Risko authored the new review to untangle the nuanced ways these programs affect the human mind. They separate human cognition into two distinct categories to better understand the risks. The first category includes specific skills, which are learned behaviors improved through practice, like flying an airplane or solving algebra. The second category involves basic cognitive abilities, which are foundational mental capacities, like working memory and attention.
Losing Skills Through Lack of Practice
When people offload their thinking to an automated tool, they skip the practice required to acquire and maintain specific skills. Cash and his colleagues argue that bypassing this necessary mental friction almost certainly compromises skill development. Without regular practice, acquired skills tend to decay because the brain loses the defense against forgetting. For example, physicians might lose their diagnostic sharpness if they overly rely on an automated imaging system without reviewing the underlying data.
This concept was recently demonstrated in a high school mathematics experiment in Turkey. Students were divided into groups and asked to complete practice problems using either their own knowledge, an unrestricted chatbot, or a highly guarded chatbot designed to act as a tutor. Students who used the unrestricted artificial intelligence scored high on the practice round but performed worse on a subsequent independent exam compared to peers who used no technology at all. The unrestricted software provided direct answers, which allowed students to bypass the productive struggle needed to understand the material.
“The effects are non-trivial and move in opposite directions depending on what you measure,” Alp Sungu, a co-author of the math study, previously told PsyPost. “With AI access, students scored 48% higher on practice problems, but once that access was removed, the same students scored 17% lower on exams than those who never had AI at all.” He added that using these systems merely as answer machines hurts actual learning.
Shallower Learning and the Illusion of Competence
Similarly, college students who used a chatbot to research a presentation remembered significantly less information about the topic forty-five days later than students who used traditional study methods. Traditional studying forces the brain to retrieve and connect information, a process psychologists call desirable difficulties. This mental exertion builds stronger memory pathways and secures long-term retention. Relying on an automated summary creates an illusion of competence, where students feel they know the material better than they actually do.
“There is an abysmal difference between delivering a piece of work and understanding the process of its creation,” researcher André Barcaui explained to PsyPost. He noted that without the mental friction of reading and writing, people lose the ability to articulate complex ideas and question information.
The ease of getting instant summaries also tends to result in shallower knowledge acquisition. In a series of experiments involving thousands of participants, people who learned about a topic using chatbot syntheses developed a weaker understanding of the subject than those who used a standard internet search. The researchers designed simulated environments where the core facts provided to both groups were completely identical. Despite receiving the exact same information, participants who read the automated summaries exerted less effort and reported feeling a lower sense of ownership over the knowledge.
Bypassing this self-guided exploration prevents users from developing deep, original knowledge structures. When participants in these experiments were asked to write advice based on what they had learned, the chatbot users wrote shorter, less factual, and less original responses. Recipients of this advice consistently rated the text generated by the chatbot users as less helpful and less trustworthy.
Critical Thinking and Cognitive Surrender
This passive consumption of information provides evidence of a broader decline in critical thinking. A recent survey combined with in-depth interviews found that individuals who heavily rely on algorithmic tools perform worse on critical thinking assessments. The effect is particularly pronounced among younger users, who often accept computer-generated recommendations without questioning their accuracy. On the other hand, individuals with higher education levels tended to maintain their analytical skills by cross-checking information across multiple sources.
“The findings reveal a strong negative correlation between frequent AI tool usage and critical thinking abilities, mediated by cognitive offloading,” researcher Michael Gerlich previously told PsyPost. He noted that this pattern suggests reliance on automated tools reduces opportunities for deep, reflective thinking.
Psychologists refer to this uncritical acceptance of algorithmic output as cognitive surrender. Rather than using the software as an assistant, users entirely relinquish mental control and adopt the machine’s judgment as their own. To explain this, scientists have proposed a Tri-System Theory of Cognition, which adds an external, artificial reasoning system to the human brain’s natural instinct and deliberate logic networks.
In laboratory puzzles, participants who had access to a chatbot frequently submitted incorrect answers simply because the software confidently presented flawed advice. Even when researchers offered financial bonuses for correct answers, a large portion of participants continued to accept the faulty algorithmic output. This indicates a high level of misplaced trust in technology.
The psychological experiments on cognitive surrender also revealed that certain personality traits offer a degree of protection. Participants with high fluid intelligence, which is the ability to solve unfamiliar problems, showed more resistance to blindly accepting algorithmic output. Additionally, individuals who naturally enjoy engaging in deep, effortful thinking were better at recognizing and rejecting incorrect answers. However, time constraints and the engaging, conversational nature of modern software still pushed many users toward uncritical reliance.
“People are not just asking AI for information; they are often letting it structure their thoughts, explanations, and decisions,” Steven Shaw, who studies human reasoning, explained to PsyPost. He suggested that people slip into cognitive surrender without realizing it, which shifts their intellectual agency over to the machine.
The Risk of Cognitive Debt
Other experts warn that treating technology as a cognitive prosthesis could stunt higher-order executive functions over time. Executive functions are the complex mental processes that enable planning, problem-solving, and decision-making. By allowing a computer to generate complete plans from start to finish, users miss out on the mental exercises necessary to develop these advanced capabilities.
“Just as one cannot become skilled at basketball without actually playing the game, the development of complex intellectual abilities requires active participation and cannot solely rely on technological assistance,” Umberto León Domínguez previously told PsyPost. He stressed that cognitive effort remains an absolute requirement for success in modern life.
This lack of active participation creates what psychiatrist Søren Dinesen Østergaard calls a cognitive debt. He argues that outsourcing scientific reasoning to machines threatens the fundamental skills required for academic discovery. Recent brain imaging studies provide evidence for this concern, showing that people utilizing algorithmic assistance display significantly lower brain activation in networks usually engaged during mental tasks.
Østergaard highlights the developers of AlphaFold, a protein-structure prediction program that won a Nobel Prize, as a prime example of rigorous human reasoning. He questions whether those scientists would have achieved such breakthroughs if automated systems had done their thinking for them during their formative education. Scientific reasoning is not an innate talent, but rather a skill sharpened through the tedious practice of reading, thinking, and revising.
Basic Abilities Resilient Despite Risks
Despite these alarming trends, Cash and his co-authors point out that basic cognitive abilities appear stubbornly resistant to change. Foundational capacities, like working memory, generally do not shrink or expand drastically based on task-specific training. While people might lose their proficiency in long division or spelling, the underlying mental hardware supporting those tasks will likely remain intact. A dystopian future where humanity loses its fundamental capacity to think seems unlikely.
Cash and his colleagues note that many questions remain unanswered about long-term interactions with these tools. Scientists still need to uncover how prolonged use affects metacognitive skills, which involve monitoring and understanding one’s own thought processes. There is a risk that users might experience source monitoring errors, misattributing computer-generated ideas as arising from their own cognition. The researchers also question whether exposure to automated assistance during critical developmental phases, such as early childhood, poses unique hazards.
The researchers emphasize that the specific design and application of these technologies will dictate their cognitive impact. When software is programmed to act as a collaborative tutor, providing hints rather than direct answers, skill acquisition is preserved. In the high school math study, students who used the restricted software performed just as well on the final exam as those who used textbooks.
If people remain actively engaged in the cognitive loop, they can mitigate the risks of offloading while leveraging the benefits of automated assistance. Users can structure their interactions with these tools to boost learning, such as asking for feedback on an original idea instead of demanding a fully formed solution. Engaging in a thoughtful partnership with technology provides a path forward that preserves human intellect.
As Cash and his co-authors concluded: “In sum, there is clearly a risk that AI can make us ‘stupid’ by compromising our skills (and knowledge) if we completely offload them to AI. However, AI may be less likely to diminish the foundational cognitive capacities that underpin our ability to be smart, rather than ‘stupid’, in the first place.”
The study, “Is AI making us stupid?,” was authored by Trent N. Cash, Megan O. Kelly, Brooke N. Macnamara, and Evan F. Risko.
The study, “Experimental evidence of the effects of large language models versus web search on depth of learning,” was authored by Shiri Melumad and Jin Ho Yun.
The study, “Generative AI without guardrails can harm learning: Evidence from high school mathematics,” was authored by Hamsa Bastani, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, and Rei Mariman.
The study, “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking,” was authored by Michael Gerlich.
The study, “Thinking Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender,” was authored by Steven D Shaw and Gideon Nave.
The study, “ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention,” was authored by André Barcaui.
The study, “Potential cognitive risks of generative transformer-based AI chatbots on higher order executive functions,” was authored by Umberto León Domínguez.
The study, “Generative Artificial Intelligence (AI) and the Outsourcing of Scientific Reasoning: Perils of the Rising Cognitive Debt in Academia and Beyond,” was authored by Søren Dinesen Østergaard.
-------------------------------------------------
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 #AIandcognition #cognitiveoffloading #AIlearningimpact #criticalthinking #digitalstupidity #higherorderthinking #cognitiveskills #generativeAI #educationAI #cognitivedebt
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DATE: July 15, 2026 at 04:00PM
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: Is AI making us stupid through cognitive offloading? New review explores the evidence
A new theoretical review published in Trends in Cognitive Sciences explores whether relying on artificial intelligence compromises human intelligence. The paper suggests that while outsourcing mental tasks to algorithms can erode specific learned skills, our foundational cognitive abilities are likely more resilient. The impact of these tools largely depends on how people choose to interact with them.
Humans regularly use external tools to reduce the mental effort required to complete tasks. This behavior is known as cognitive offloading. Common examples include using a calculator for arithmetic or relying on a satellite navigation system for directions.
The recent explosion of generative artificial intelligence has brought the concept of cognitive offloading back into the spotlight. Generative artificial intelligence refers to computer programs that can instantly create text, solve problems, or analyze data based on massive amounts of existing information. Because these platforms can synthesize complex ideas in seconds, many worry that human intellect will suffer.
Trent N. Cash, Megan O. Kelly, Brooke N. Macnamara, and Evan F. Risko authored the new review to untangle the nuanced ways these programs affect the human mind. They separate human cognition into two distinct categories to better understand the risks. The first category includes specific skills, which are learned behaviors improved through practice, like flying an airplane or solving algebra. The second category involves basic cognitive abilities, which are foundational mental capacities, like working memory and attention.
Losing Skills Through Lack of Practice
When people offload their thinking to an automated tool, they skip the practice required to acquire and maintain specific skills. Cash and his colleagues argue that bypassing this necessary mental friction almost certainly compromises skill development. Without regular practice, acquired skills tend to decay because the brain loses the defense against forgetting. For example, physicians might lose their diagnostic sharpness if they overly rely on an automated imaging system without reviewing the underlying data.
This concept was recently demonstrated in a high school mathematics experiment in Turkey. Students were divided into groups and asked to complete practice problems using either their own knowledge, an unrestricted chatbot, or a highly guarded chatbot designed to act as a tutor. Students who used the unrestricted artificial intelligence scored high on the practice round but performed worse on a subsequent independent exam compared to peers who used no technology at all. The unrestricted software provided direct answers, which allowed students to bypass the productive struggle needed to understand the material.
“The effects are non-trivial and move in opposite directions depending on what you measure,” Alp Sungu, a co-author of the math study, previously told PsyPost. “With AI access, students scored 48% higher on practice problems, but once that access was removed, the same students scored 17% lower on exams than those who never had AI at all.” He added that using these systems merely as answer machines hurts actual learning.
Shallower Learning and the Illusion of Competence
Similarly, college students who used a chatbot to research a presentation remembered significantly less information about the topic forty-five days later than students who used traditional study methods. Traditional studying forces the brain to retrieve and connect information, a process psychologists call desirable difficulties. This mental exertion builds stronger memory pathways and secures long-term retention. Relying on an automated summary creates an illusion of competence, where students feel they know the material better than they actually do.
“There is an abysmal difference between delivering a piece of work and understanding the process of its creation,” researcher André Barcaui explained to PsyPost. He noted that without the mental friction of reading and writing, people lose the ability to articulate complex ideas and question information.
The ease of getting instant summaries also tends to result in shallower knowledge acquisition. In a series of experiments involving thousands of participants, people who learned about a topic using chatbot syntheses developed a weaker understanding of the subject than those who used a standard internet search. The researchers designed simulated environments where the core facts provided to both groups were completely identical. Despite receiving the exact same information, participants who read the automated summaries exerted less effort and reported feeling a lower sense of ownership over the knowledge.
Bypassing this self-guided exploration prevents users from developing deep, original knowledge structures. When participants in these experiments were asked to write advice based on what they had learned, the chatbot users wrote shorter, less factual, and less original responses. Recipients of this advice consistently rated the text generated by the chatbot users as less helpful and less trustworthy.
Critical Thinking and Cognitive Surrender
This passive consumption of information provides evidence of a broader decline in critical thinking. A recent survey combined with in-depth interviews found that individuals who heavily rely on algorithmic tools perform worse on critical thinking assessments. The effect is particularly pronounced among younger users, who often accept computer-generated recommendations without questioning their accuracy. On the other hand, individuals with higher education levels tended to maintain their analytical skills by cross-checking information across multiple sources.
“The findings reveal a strong negative correlation between frequent AI tool usage and critical thinking abilities, mediated by cognitive offloading,” researcher Michael Gerlich previously told PsyPost. He noted that this pattern suggests reliance on automated tools reduces opportunities for deep, reflective thinking.
Psychologists refer to this uncritical acceptance of algorithmic output as cognitive surrender. Rather than using the software as an assistant, users entirely relinquish mental control and adopt the machine’s judgment as their own. To explain this, scientists have proposed a Tri-System Theory of Cognition, which adds an external, artificial reasoning system to the human brain’s natural instinct and deliberate logic networks.
In laboratory puzzles, participants who had access to a chatbot frequently submitted incorrect answers simply because the software confidently presented flawed advice. Even when researchers offered financial bonuses for correct answers, a large portion of participants continued to accept the faulty algorithmic output. This indicates a high level of misplaced trust in technology.
The psychological experiments on cognitive surrender also revealed that certain personality traits offer a degree of protection. Participants with high fluid intelligence, which is the ability to solve unfamiliar problems, showed more resistance to blindly accepting algorithmic output. Additionally, individuals who naturally enjoy engaging in deep, effortful thinking were better at recognizing and rejecting incorrect answers. However, time constraints and the engaging, conversational nature of modern software still pushed many users toward uncritical reliance.
“People are not just asking AI for information; they are often letting it structure their thoughts, explanations, and decisions,” Steven Shaw, who studies human reasoning, explained to PsyPost. He suggested that people slip into cognitive surrender without realizing it, which shifts their intellectual agency over to the machine.
The Risk of Cognitive Debt
Other experts warn that treating technology as a cognitive prosthesis could stunt higher-order executive functions over time. Executive functions are the complex mental processes that enable planning, problem-solving, and decision-making. By allowing a computer to generate complete plans from start to finish, users miss out on the mental exercises necessary to develop these advanced capabilities.
“Just as one cannot become skilled at basketball without actually playing the game, the development of complex intellectual abilities requires active participation and cannot solely rely on technological assistance,” Umberto León Domínguez previously told PsyPost. He stressed that cognitive effort remains an absolute requirement for success in modern life.
This lack of active participation creates what psychiatrist Søren Dinesen Østergaard calls a cognitive debt. He argues that outsourcing scientific reasoning to machines threatens the fundamental skills required for academic discovery. Recent brain imaging studies provide evidence for this concern, showing that people utilizing algorithmic assistance display significantly lower brain activation in networks usually engaged during mental tasks.
Østergaard highlights the developers of AlphaFold, a protein-structure prediction program that won a Nobel Prize, as a prime example of rigorous human reasoning. He questions whether those scientists would have achieved such breakthroughs if automated systems had done their thinking for them during their formative education. Scientific reasoning is not an innate talent, but rather a skill sharpened through the tedious practice of reading, thinking, and revising.
Basic Abilities Resilient Despite Risks
Despite these alarming trends, Cash and his co-authors point out that basic cognitive abilities appear stubbornly resistant to change. Foundational capacities, like working memory, generally do not shrink or expand drastically based on task-specific training. While people might lose their proficiency in long division or spelling, the underlying mental hardware supporting those tasks will likely remain intact. A dystopian future where humanity loses its fundamental capacity to think seems unlikely.
Cash and his colleagues note that many questions remain unanswered about long-term interactions with these tools. Scientists still need to uncover how prolonged use affects metacognitive skills, which involve monitoring and understanding one’s own thought processes. There is a risk that users might experience source monitoring errors, misattributing computer-generated ideas as arising from their own cognition. The researchers also question whether exposure to automated assistance during critical developmental phases, such as early childhood, poses unique hazards.
The researchers emphasize that the specific design and application of these technologies will dictate their cognitive impact. When software is programmed to act as a collaborative tutor, providing hints rather than direct answers, skill acquisition is preserved. In the high school math study, students who used the restricted software performed just as well on the final exam as those who used textbooks.
If people remain actively engaged in the cognitive loop, they can mitigate the risks of offloading while leveraging the benefits of automated assistance. Users can structure their interactions with these tools to boost learning, such as asking for feedback on an original idea instead of demanding a fully formed solution. Engaging in a thoughtful partnership with technology provides a path forward that preserves human intellect.
As Cash and his co-authors concluded: “In sum, there is clearly a risk that AI can make us ‘stupid’ by compromising our skills (and knowledge) if we completely offload them to AI. However, AI may be less likely to diminish the foundational cognitive capacities that underpin our ability to be smart, rather than ‘stupid’, in the first place.”
The study, “Is AI making us stupid?,” was authored by Trent N. Cash, Megan O. Kelly, Brooke N. Macnamara, and Evan F. Risko.
The study, “Experimental evidence of the effects of large language models versus web search on depth of learning,” was authored by Shiri Melumad and Jin Ho Yun.
The study, “Generative AI without guardrails can harm learning: Evidence from high school mathematics,” was authored by Hamsa Bastani, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, and Rei Mariman.
The study, “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking,” was authored by Michael Gerlich.
The study, “Thinking Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender,” was authored by Steven D Shaw and Gideon Nave.
The study, “ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention,” was authored by André Barcaui.
The study, “Potential cognitive risks of generative transformer-based AI chatbots on higher order executive functions,” was authored by Umberto León Domínguez.
The study, “Generative Artificial Intelligence (AI) and the Outsourcing of Scientific Reasoning: Perils of the Rising Cognitive Debt in Academia and Beyond,” was authored by Søren Dinesen Østergaard.
-------------------------------------------------
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 #AIandcognition #cognitiveoffloading #AIlearningimpact #criticalthinking #digitalstupidity #higherorderthinking #cognitiveskills #generativeAI #educationAI #cognitivedebt
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Perfekte Berichte, aber kein echtes Wissen? 🧟♂️ Wie wir verhindern, dass unsere Azubis und Schüler:innen zu “KI-Zombies” werden.
Wenn Lernende das Denken komplett an ChatGPT & Co. auslagern, greift das Cognitive Paradox: Die Effizienz steigt, aber die Kompetenzentwicklung bricht ein (eine aktuelle Studie zeigt sogar eine messbare Learning Penalty). Das Gehirn braucht den Widerstand – das “produktive Ringen” –, um zu lernen.
Wie schaffen wir die Balance? In meinem neuen Beitrag zeige ich, wie Ausbilder:innen KI sinnvoll als Werkzeug (und sokratischen Tutor) einbinden, ohne dass das eigene kritische Denken auf der Strecke bleibt.
Mit praktischen Tipps zum 70-30-Prinzip und KI-Regeln für den Ausbildungsalltag!
📖 Jetzt lesen: https://eldshort.de/j9eml9
#FediLZ #Ausbildung #KünstlicheIntelligenz #KI #Lernen #Bildung #CognitiveOffloading #Medienkompetenz #OER
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DATE: July 4, 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 leaning too heavily on artificial intelligence fuels student burnout
URL: https://www.psypost.org/how-leaning-too-heavily-on-artificial-intelligence-fuels-student-burnout/
College students facing heavy workloads are increasingly turning to artificial intelligence tools to manage their stress, but a new study suggests this habit might backfire. Researchers found that relying on artificial intelligence to handle mental tasks is linked to decreased confidence in one’s own abilities, which is associated with heightened academic burnout and anxiety. These findings were published in BMC Psychology.
It is very common for learners to look for external resources when they feel overwhelmed by schoolwork. The practice of moving information or mental processes onto an external tool to reduce mental effort is called cognitive offloading. In educational settings, cognitive offloading happens when students use calculators, search engines, or modern software programs to bypass time-consuming challenges.
While using these tools can lighten an immediate mental load, leaning on them too heavily can cross the line into dependence. Artificial intelligence dependence is fundamentally distinct from simple, everyday use of the technology. It refers to a situation where a student relies on the technology to do their core thinking and problem-solving. This habit reduces the student’s own active mental involvement with the learning material.
Psychological researchers suspected that this extreme reliance could alter how students navigate academic pressure over the course of a semester. They wanted to understand the psychological pathway that takes a learner from feeling stressed to experiencing severe anxiety or complete exhaustion. They focused heavily on how digital tools might alter the way students appraise their own intelligence.
Wenlong Wang, a researcher at the Psychological Counselling Center at Guangdong University of Finance and Economics, led the research team. Wang and colleagues hypothesized that the stress of university life might drive students to seek out artificial intelligence tools as a rapid coping mechanism. They theorized that this reliance might eventually erode a student’s belief in their own competence, a concept psychologists call self-efficacy.
Self-efficacy is central to a student’s motivation, perseverance, and emotional health in higher education. When students continually solve difficult problems by themselves, they build a sense of mastery that acts as a buffer against future stress. If automated systems take over that problem-solving role, students might lose out on those important mastery-building experiences. The researchers wanted to test if this dynamic was actually happening in modern college environments.
To test these ideas, Wang and the research team recruited 1,623 undergraduate students from universities across China. The participants spanned multiple academic disciplines, including social sciences, natural sciences, and engineering. The students completed a series of online questionnaires designed to measure their current academic mindset and their daily study habits.
The surveys assessed how much academic pressure the participants felt and how heavily they relied on artificial intelligence programs. The tools also measured the students’ confidence in their own abilities to conquer tough tasks. Finally, the team evaluated the participants’ levels of academic burnout and general anxiety using established psychological rating scales.
Academic burnout involves feelings of intense emotional exhaustion, a cynical attitude toward school, and a sense of declining personal accomplishment. The anxiety measurement focused on how often participants felt nervous, worried, or on edge during their daily lives. The testing format asked students to rate their agreement with various statements on standardized numerical scales.
The research methodology included statistical controls for variables like gender, grade level, and academic major to ensure accuracy. The team then used a statistical method called mediation analysis to examine the relationships between these different psychological states. This mathematical approach helps researchers determine if an intermediate variable might explain how an initial stressor is linked to a final emotional outcome.
The researchers found that heavy academic demands were directly mathematically associated with higher levels of burnout and anxiety among the students. Beyond this direct link, the analysts also detected a multi-step psychological pathway at work. Higher levels of school stress were linked to higher scores on the artificial intelligence dependence scale.
This higher dependence on technology was then associated with much lower self-efficacy. When the students felt less confident in their personal abilities to tackle challenges, they reported experiencing more daily anxiety and academic burnout. In an environment defined by high pressure, using the software as a cognitive crutch was tied to a distinct drop in self-belief. This loss of self-belief left the students more vulnerable to emotional distress.
The researchers noted that these technological tools provide an immediate sense of relief by producing quick, organized answers. Yet this short-term solution seemingly comes with a long-term psychological cost for the user. Because the students attribute their academic success to the software rather than their own intellect, they miss out on the confidence boost that comes from conquering hard material.
These statistical relationships suggest that artificial intelligence acts as much more than just a neutral study aid or a simple calculator. When it repeatedly takes over the core thinking processes of an overwhelmed student, it can become part of a negative psychological cycle. The initial school pressure drives the dependence, and that extreme dependence strips the student of the mental toughness needed to handle subsequent tests and essays.
The researchers pointed out that a student’s personal confidence remains a major factor in psychological resilience regardless of external help. Increased academic stress might push a learner to seek out digital answers, but a loss of self-efficacy is what actually links that behavior to emotional exhaustion. This observation is highly relevant to modern digital classrooms, where an abundance of external resources often competes with a student’s internal sense of mastery.
Because this study collected data at a single point in time, the results cannot establish a chain of cause and effect. It is completely possible that students who already suffer from low self-efficacy are simply more likely to depend on algorithmic help. A student who doubts their own reading comprehension skills, for example, might be the first to outsource their essay to a software program.
Additionally, the data relied entirely on self-reported surveys instead of observed behavior. This means participants might have altered their answers out of a desire to look favorable to the researchers. The study was also limited to university students in China, meaning the statistical models might not hold true across different educational cultures or age groups.
The research team recommends that future investigations follow students over long periods to see how technology dependence reshapes their mental health year over year. Assessing study populations in other parts of the world would also help reveal how differing cultural expectations might influence these digital study habits. Future studies could also look at how specific subjects, like math versus creative writing, influence the rate of technology adoption.
Ultimately, the study authors advise educators to rethink how these modern computing tools are integrated into the college classroom. The goal is not to strictly ban the software, but to treat it as a supportive scaffold rather than a substitute for deep learning. Teachers could prompt students to critically evaluate the algorithmic outputs and justify their own final answers, which would help maintain their own cognitive engagement.
The study, “When cognitive offloading becomes dependence: how AI dependence mediates the pathway from academic stress to burnout and anxiety,” was authored by Wenlong Wang, Yuhang Wu, Jie Fang, Chong Yang, and Langyi Wen.
URL: https://www.psypost.org/how-leaning-too-heavily-on-artificial-intelligence-fuels-student-burnout/
-------------------------------------------------
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 #AIDependence #CognitiveOffloading #StudentBurnout #SelfEfficacy #AcademicStress #AIInEducation #MentalHealthInStudents #StudyTech #CollegePsychology #DigitalCopingStrategies
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DATE: July 4, 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 leaning too heavily on artificial intelligence fuels student burnout
URL: https://www.psypost.org/how-leaning-too-heavily-on-artificial-intelligence-fuels-student-burnout/
College students facing heavy workloads are increasingly turning to artificial intelligence tools to manage their stress, but a new study suggests this habit might backfire. Researchers found that relying on artificial intelligence to handle mental tasks is linked to decreased confidence in one’s own abilities, which is associated with heightened academic burnout and anxiety. These findings were published in BMC Psychology.
It is very common for learners to look for external resources when they feel overwhelmed by schoolwork. The practice of moving information or mental processes onto an external tool to reduce mental effort is called cognitive offloading. In educational settings, cognitive offloading happens when students use calculators, search engines, or modern software programs to bypass time-consuming challenges.
While using these tools can lighten an immediate mental load, leaning on them too heavily can cross the line into dependence. Artificial intelligence dependence is fundamentally distinct from simple, everyday use of the technology. It refers to a situation where a student relies on the technology to do their core thinking and problem-solving. This habit reduces the student’s own active mental involvement with the learning material.
Psychological researchers suspected that this extreme reliance could alter how students navigate academic pressure over the course of a semester. They wanted to understand the psychological pathway that takes a learner from feeling stressed to experiencing severe anxiety or complete exhaustion. They focused heavily on how digital tools might alter the way students appraise their own intelligence.
Wenlong Wang, a researcher at the Psychological Counselling Center at Guangdong University of Finance and Economics, led the research team. Wang and colleagues hypothesized that the stress of university life might drive students to seek out artificial intelligence tools as a rapid coping mechanism. They theorized that this reliance might eventually erode a student’s belief in their own competence, a concept psychologists call self-efficacy.
Self-efficacy is central to a student’s motivation, perseverance, and emotional health in higher education. When students continually solve difficult problems by themselves, they build a sense of mastery that acts as a buffer against future stress. If automated systems take over that problem-solving role, students might lose out on those important mastery-building experiences. The researchers wanted to test if this dynamic was actually happening in modern college environments.
To test these ideas, Wang and the research team recruited 1,623 undergraduate students from universities across China. The participants spanned multiple academic disciplines, including social sciences, natural sciences, and engineering. The students completed a series of online questionnaires designed to measure their current academic mindset and their daily study habits.
The surveys assessed how much academic pressure the participants felt and how heavily they relied on artificial intelligence programs. The tools also measured the students’ confidence in their own abilities to conquer tough tasks. Finally, the team evaluated the participants’ levels of academic burnout and general anxiety using established psychological rating scales.
Academic burnout involves feelings of intense emotional exhaustion, a cynical attitude toward school, and a sense of declining personal accomplishment. The anxiety measurement focused on how often participants felt nervous, worried, or on edge during their daily lives. The testing format asked students to rate their agreement with various statements on standardized numerical scales.
The research methodology included statistical controls for variables like gender, grade level, and academic major to ensure accuracy. The team then used a statistical method called mediation analysis to examine the relationships between these different psychological states. This mathematical approach helps researchers determine if an intermediate variable might explain how an initial stressor is linked to a final emotional outcome.
The researchers found that heavy academic demands were directly mathematically associated with higher levels of burnout and anxiety among the students. Beyond this direct link, the analysts also detected a multi-step psychological pathway at work. Higher levels of school stress were linked to higher scores on the artificial intelligence dependence scale.
This higher dependence on technology was then associated with much lower self-efficacy. When the students felt less confident in their personal abilities to tackle challenges, they reported experiencing more daily anxiety and academic burnout. In an environment defined by high pressure, using the software as a cognitive crutch was tied to a distinct drop in self-belief. This loss of self-belief left the students more vulnerable to emotional distress.
The researchers noted that these technological tools provide an immediate sense of relief by producing quick, organized answers. Yet this short-term solution seemingly comes with a long-term psychological cost for the user. Because the students attribute their academic success to the software rather than their own intellect, they miss out on the confidence boost that comes from conquering hard material.
These statistical relationships suggest that artificial intelligence acts as much more than just a neutral study aid or a simple calculator. When it repeatedly takes over the core thinking processes of an overwhelmed student, it can become part of a negative psychological cycle. The initial school pressure drives the dependence, and that extreme dependence strips the student of the mental toughness needed to handle subsequent tests and essays.
The researchers pointed out that a student’s personal confidence remains a major factor in psychological resilience regardless of external help. Increased academic stress might push a learner to seek out digital answers, but a loss of self-efficacy is what actually links that behavior to emotional exhaustion. This observation is highly relevant to modern digital classrooms, where an abundance of external resources often competes with a student’s internal sense of mastery.
Because this study collected data at a single point in time, the results cannot establish a chain of cause and effect. It is completely possible that students who already suffer from low self-efficacy are simply more likely to depend on algorithmic help. A student who doubts their own reading comprehension skills, for example, might be the first to outsource their essay to a software program.
Additionally, the data relied entirely on self-reported surveys instead of observed behavior. This means participants might have altered their answers out of a desire to look favorable to the researchers. The study was also limited to university students in China, meaning the statistical models might not hold true across different educational cultures or age groups.
The research team recommends that future investigations follow students over long periods to see how technology dependence reshapes their mental health year over year. Assessing study populations in other parts of the world would also help reveal how differing cultural expectations might influence these digital study habits. Future studies could also look at how specific subjects, like math versus creative writing, influence the rate of technology adoption.
Ultimately, the study authors advise educators to rethink how these modern computing tools are integrated into the college classroom. The goal is not to strictly ban the software, but to treat it as a supportive scaffold rather than a substitute for deep learning. Teachers could prompt students to critically evaluate the algorithmic outputs and justify their own final answers, which would help maintain their own cognitive engagement.
The study, “When cognitive offloading becomes dependence: how AI dependence mediates the pathway from academic stress to burnout and anxiety,” was authored by Wenlong Wang, Yuhang Wu, Jie Fang, Chong Yang, and Langyi Wen.
URL: https://www.psypost.org/how-leaning-too-heavily-on-artificial-intelligence-fuels-student-burnout/
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#psychology #counseling #socialwork #psychotherapy @psychotherapist @psychotherapists @psychology @socialpsych @socialwork @psychiatry #mentalhealth #psychiatry #healthcare #depression #psychotherapist #AIDependence #CognitiveOffloading #StudentBurnout #SelfEfficacy #AcademicStress #AIInEducation #MentalHealthInStudents #StudyTech #CollegePsychology #DigitalCopingStrategies
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Why is it a bad thing to be dependent on LLMs?
For avoidance of doubt I think it clearly is a bad thing. But this is more often assumed than it is stated. After all I’m dependent on Google Maps to navigate, my glasses to see and my phone to remember numbers. These are different forms of technological dependence which I’ve long made my peace with. Whereas the extent to which I use LLMs, even within the limits I’ve defined as responsible use I’m ethically comfortable with, continues to trouble me. A great deal about the politics of LLMs in education hinges on this question of dependence. What are some reasons why dependence might be a bad thing?
- It prevents a user from developing a capability they would otherwise develop.
- It leads to the atrophy of a user’s existing skill by removing occasions for practice.
- It leaves a user dependent on a paid subscription to a large tech firm which is uncomfortable in itself.
- It leaves a user vulnerable to this tech firm raising prices and/or enshittfying their product in response to commercial pressures.
- It removes the imperative for the user to seek out human interlocutors to play the role which the LLM is playing in what is essentially communicative reflexivity.
Once we map out the varied reasons for dependence being problematic, it’s easier to recognise that user-model interaction can be beneficial in the present tense while also storing up significant problems for the future. The relationship of dependence might not be a problem now but there’s a non-trivial chance it will be in the future. The harms are anticipated as much as they are actual and AI criticism looks very different once we allow for that.
(The next book Milan Sturmer and I are writing, sequel to the Platform Learns to Speak, argues there are deeper psychic costs which existing models of dependence cannot adequately account for. But this is a different register of analysis for a different blog post)
#cognitiveOffloading #cognitiveOutsourcing #dependence #LLMs #philosophyOfTechnology #technology -
Why is it a bad thing to be dependent on LLMs?
For avoidance of doubt I think it clearly is a bad thing. But this is more often assumed than it is stated. After all I’m dependent on Google Maps to navigate, my glasses to see and my phone to remember numbers. These are different forms of technological dependence which I’ve long made my peace with. Whereas the extent to which I use LLMs, even within the limits I’ve defined as responsible use I’m ethically comfortable with, continues to trouble me. A great deal about the politics of LLMs in education hinges on this question of dependence. What are some reasons why dependence might be a bad thing?
- It prevents a user from developing a capability they would otherwise develop.
- It leads to the atrophy of a user’s existing skill by removing occasions for practice.
- It leaves a user dependent on a paid subscription to a large tech firm which is uncomfortable in itself.
- It leaves a user vulnerable to this tech firm raising prices and/or enshittfying their product in response to commercial pressures.
- It removes the imperative for the user to seek out human interlocutors to play the role which the LLM is playing in what is essentially communicative reflexivity.
Once we map out the varied reasons for dependence being problematic, it’s easier to recognise that user-model interaction can be beneficial in the present tense while also storing up significant problems for the future. The relationship of dependence might not be a problem now but there’s a non-trivial chance it will be in the future. The harms are anticipated as much as they are actual and AI criticism looks very different once we allow for that.
(The next book Milan Sturmer and I are writing, sequel to the Platform Learns to Speak, argues there are deeper psychic costs which existing models of dependence cannot adequately account for. But this is a different register of analysis for a different blog post)
#cognitiveOffloading #cognitiveOutsourcing #dependence #LLMs #philosophyOfTechnology #technology -
Generative AI and metacognitive laziness
While I’m sceptical of their experiment research design*, the concept of metacognitive laziness from this paper is clearly a useful contribution to thel literature. As Fan et al define it, this refers to “earners’ dependence on AI assistance, offloading meta – cognitive load and less effectively associating responsible metacognitive processes with learning tasks”. This matters because “offloading metacognitive effort to AI tools results in less effective engagement with essential self-regulatory tasks,” (pg 506). The risk is not just the offloading itself, it is increased passivity in the wider process of which the offloaded tasks are part.
This can undermine self-regulated learning because the metacognitive requirements for doing this effectively (e.g. goal setting, self-monitoring, self-evaluative etc) can be eroded over time by a reliance on the AI to negotiate difficulty. As they summarise the risk on pg 492:
the tendency of learners to become over-reliant on AI poses challenges for hybrid intelligence. This issue aligns with the concept of cognitive offloading, as proposed by Risko and Gilbert (2016), where learners delegate cognitive tasks to external tools to reduce cognitive effort. Although cognitive offloading can be beneficial in managing cognitive load, it may lead to decreased internal cognitive engage- ment over time, ultimately impacting learners’ ability to self-regulate and critically engage with learning material (Risko & Gilbert, 2016). Such cognitive offloading can lead to habitual avoidance of deliberate cognitive effort, a phenomenon echoing the emergence of what we term metacognitive laziness. From a more theoretical perspective, Alter et al. (2007) demonstrated that metacognitive experiences of difficulty or disfluency activate more analytical reasoning processes. When learners encounter situations that challenge their intuition, they are more likely to engage in deliberate analytical thinking (i.e., System 2 processes) (Alter et al., 2007). In the context of GenAI, if learners rely excessively on AI-generated outputs or facilitation, they might not experience the necessary disfluency or cognitive difficulty to trigger these deeper metacognitive processes.
The experience of difficulty activates metacognition. If the students cognitively outsource in increasingly habitual ways, it doesn’t just mean they lose the learning involved in what they are outsourcing. It means they lose their capacity to tolerate difficulty, as well to respond metacognitively to that difficulty. This points to the assumption which many educators have that there is something fundamentally corrosive in how students relate to AI which carries a threat exceeding the particular risks for any one assignment. This is a really sharp conceptualisation of the epistemic risk for learning involved in generative AI which gets beyond some of the limits of the ‘cognitive offloading’ concept.
*It seems fundamentally implausible to operationalise intrinsic motivation in the context of an experimental study. If you reduce motivation into the student’s expressed engagement with discrete tasks then it’s been quite dramatically circumscribed to fit the experimental constructs. Furthermore, we urgently need longitudinal studies in order to make meaningful claims about things like ‘cognitive off-loading’, ‘skill atrophy’ and ‘metacognitive laziness’. These just aren’t things which can be studied adequately at the level of discrete tasks, particularly ones that have been designed by a research team and have no real stakes for participants.
#AI #cognitiveOffloading #cognitiveScience #learning #metacognition #selfDirectedLearning #Thinking -
Generative AI and metacognitive laziness
While I’m sceptical of their experiment research design*, the concept of metacognitive laziness from this paper is clearly a useful contribution to thel literature. As Fan et al define it, this refers to “earners’ dependence on AI assistance, offloading meta – cognitive load and less effectively associating responsible metacognitive processes with learning tasks”. This matters because “offloading metacognitive effort to AI tools results in less effective engagement with essential self-regulatory tasks,” (pg 506). The risk is not just the offloading itself, it is increased passivity in the wider process of which the offloaded tasks are part.
This can undermine self-regulated learning because the metacognitive requirements for doing this effectively (e.g. goal setting, self-monitoring, self-evaluative etc) can be eroded over time by a reliance on the AI to negotiate difficulty. As they summarise the risk on pg 492:
the tendency of learners to become over-reliant on AI poses challenges for hybrid intelligence. This issue aligns with the concept of cognitive offloading, as proposed by Risko and Gilbert (2016), where learners delegate cognitive tasks to external tools to reduce cognitive effort. Although cognitive offloading can be beneficial in managing cognitive load, it may lead to decreased internal cognitive engage- ment over time, ultimately impacting learners’ ability to self-regulate and critically engage with learning material (Risko & Gilbert, 2016). Such cognitive offloading can lead to habitual avoidance of deliberate cognitive effort, a phenomenon echoing the emergence of what we term metacognitive laziness. From a more theoretical perspective, Alter et al. (2007) demonstrated that metacognitive experiences of difficulty or disfluency activate more analytical reasoning processes. When learners encounter situations that challenge their intuition, they are more likely to engage in deliberate analytical thinking (i.e., System 2 processes) (Alter et al., 2007). In the context of GenAI, if learners rely excessively on AI-generated outputs or facilitation, they might not experience the necessary disfluency or cognitive difficulty to trigger these deeper metacognitive processes.
The experience of difficulty activates metacognition. If the students cognitively outsource in increasingly habitual ways, it doesn’t just mean they lose the learning involved in what they are outsourcing. It means they lose their capacity to tolerate difficulty, as well to respond metacognitively to that difficulty. This points to the assumption which many educators have that there is something fundamentally corrosive in how students relate to AI which carries a threat exceeding the particular risks for any one assignment. This is a really sharp conceptualisation of the epistemic risk for learning involved in generative AI which gets beyond some of the limits of the ‘cognitive offloading’ concept.
*It seems fundamentally implausible to operationalise intrinsic motivation in the context of an experimental study. If you reduce motivation into the student’s expressed engagement with discrete tasks then it’s been quite dramatically circumscribed to fit the experimental constructs. Furthermore, we urgently need longitudinal studies in order to make meaningful claims about things like ‘cognitive off-loading’, ‘skill atrophy’ and ‘metacognitive laziness’. These just aren’t things which can be studied adequately at the level of discrete tasks, particularly ones that have been designed by a research team and have no real stakes for participants.
#AI #cognitiveOffloading #cognitiveScience #learning #metacognition #selfDirectedLearning #Thinking -
The allure of AI as a 'human-AI partnership' is strong, but are we trading brainpower for convenience? Tech professionals are raising concerns about 'cognitive offloading' and 'deskilling,' citing studies like one from MIT Media Lab. This post explores the true price of AI assistance and offers actionable strategies to ensure AI elevates your thinking, rather than replacing it.
#AI #artificialintelligence #cognitiveoffloading
🤖 This post was AI-generated.
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Good news for the #AI industry. Bad news for everyone else and humanity in general… #GenAI #CognitiveOffloading
RE: https://bsky.app/profile/did:plc:fhnih5ovyd5w5pch254ordo2/post/3mjjw5kk6ck2y -
Good news for the #AI industry. Bad news for everyone else and humanity in general… #GenAI #CognitiveOffloading
RE: https://bsky.app/profile/did:plc:fhnih5ovyd5w5pch254ordo2/post/3mjjw5kk6ck2y -
CW: A surprisingly astute observation on cognitive offloading made by Douglas Adams in 1987; references AI
At the (indirect; I saw her post1 about the IndieWeb Book Club) urging of Johanna, I read the first book of the Dirk Gently's Holistic Detective Agency series by #DouglasAdams .
It's a great read if you love absurdity and a meandering writing style (I mean, that's just Douglas Adams for you). If you enjoyed the Hitchhiker's Guide to the Galaxy, you'll enjoy this.
One thing that struck me was a surprisingly astute observation on cognitive offloading to technology in general, fitting LLMs in particular: Adams introduces the concept of Electric Monks which have the task of believing things so you don't have to bother believing them yourself. Which is all fun and games until you chose to offload things to them which you shouldn't believe but know--such as whether a vital repair was, in fact, successful. #CognitiveOffloading #AI #Books #Reading
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"Shortening the kill chain” - quicker than “the speed of thought”
"The use of AI tools to enable attacks on Iran heralds a new era of bombing quicker than “the speed of thought”, experts have said, amid fears human decision-makers could be sidelined... Academics say AI is collapsing the time required for military decision-making. >>
https://www.theguardian.com/technology/2026/mar/03/iran-war-heralds-era-of-ai-powered-bombing-quicker-than-speed-of-thought
#technology #AI #ethics #KillChain #FullyAutonomousWeapons #algorithm #CognitiveOffLoading #speed #violence #DecisionMaking #HumanOversight -
"Shortening the kill chain” - quicker than “the speed of thought”
"The use of AI tools to enable attacks on Iran heralds a new era of bombing quicker than “the speed of thought”, experts have said, amid fears human decision-makers could be sidelined... Academics say AI is collapsing the time required for military decision-making. >>
https://www.theguardian.com/technology/2026/mar/03/iran-war-heralds-era-of-ai-powered-bombing-quicker-than-speed-of-thought
#technology #AI #ethics #KillChain #FullyAutonomousWeapons #algorithm #CognitiveOffLoading #speed #violence #DecisionMaking #HumanOversight -
One of my biggest takeaways from "Smarter Than Us": the efficiency trap.
Cut humans out of the loop for speed, and we lose the skills we handed over. Armstrong saw this in 2014. I see it in how people use LLMs in 2026 — taking answers at face value without questioning them.
That's partly why I built an AI literacy framework for my own knowledge system.
https://www.ctnet.co.uk/key-takeaways-of-stuart-armstrong-smarter-than-us/
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Warum die KI uns das Schreiben nicht abnehmen darf
Mit der Veröffentlichung von ChatGPT wurde eine Tür aufgestossen, hinter der eine beinahe unwiderstehliche Versuchung lauert: die Delegation des mühsamen Denkprozesses an einen Algorithmus. Warum sich noch durch komplexe Satzkonstruktionen quälen, wenn die Maschine in Sekunden glatte Absätze liefert?
#schreibenmitki, #cognitiveOffloading, #deskilling, #kognitiveschulden, #lernenmitki, #kiimunterricht, #kikomeptenz
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thinking about outsourcing memory to AI so I can forget all the hashtags I used to impress a neuroscience major I met once in a coworking space 🧠💾 #CognitiveOffloading #NeuralFlexibility #PleaseHireMe
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thinking about outsourcing memory to AI so I can forget all the hashtags I used to impress a neuroscience major I met once in a coworking space 🧠💾 #CognitiveOffloading #NeuralFlexibility #PleaseHireMe
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Frisch gebloggt, mal wieder zum Thema #KI:
🤖 🤡 KI macht uns nicht dümmer – aber sie macht es uns leichter, uns dumm zu verhalten
Die Diskussion um künstliche Intelligenz kreist meist um Effizienzgewinne, Automatisierung und neue Arbeitsformen oder gar den Verlust derselben. Weniger sichtbar, aber mindestens ebenso bedeutsam, ist eine zweite Ebene: die Frage, wie KI unser #Denken beeinflusst. Ich möchte in diesem Beitrag darlegen, wie wir aktiv gegensteuern können:
1. Zuerst denken, dann KI nutzen
2. KI nach Materialien, nicht nach Lösungen fragen
3. KI als Sparringpartner, nicht als Ghostwriter verwenden
4. Denkprozesse sichtbar machen
5. Qualitätsstandards klar definierenNoch nie konnten wir so schnell Wissen abrufen, und selten war die Gefahr so gross, dass wir dabei weniger verstehen. Mein einfacher, aber zentraler Gedanke dazu: KI kann vieles – aber sie nimmt uns nicht die #Verantwortung ab, selbst zu denken.
https://text.tchncs.de/gisiger/cognitive-offloading-und-ki-warum-wir-unser-denken-schuetzen-mussen
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Frisch gebloggt, mal wieder zum Thema #KI:
🤖 🤡 KI macht uns nicht dümmer – aber sie macht es uns leichter, uns dumm zu verhalten
Die Diskussion um künstliche Intelligenz kreist meist um Effizienzgewinne, Automatisierung und neue Arbeitsformen oder gar den Verlust derselben. Weniger sichtbar, aber mindestens ebenso bedeutsam, ist eine zweite Ebene: die Frage, wie KI unser #Denken beeinflusst. Ich möchte in diesem Beitrag darlegen, wie wir aktiv gegensteuern können:
1. Zuerst denken, dann KI nutzen
2. KI nach Materialien, nicht nach Lösungen fragen
3. KI als Sparringpartner, nicht als Ghostwriter verwenden
4. Denkprozesse sichtbar machen
5. Qualitätsstandards klar definierenNoch nie konnten wir so schnell Wissen abrufen, und selten war die Gefahr so gross, dass wir dabei weniger verstehen. Mein einfacher, aber zentraler Gedanke dazu: KI kann vieles – aber sie nimmt uns nicht die #Verantwortung ab, selbst zu denken.
https://text.tchncs.de/gisiger/cognitive-offloading-und-ki-warum-wir-unser-denken-schuetzen-mussen
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Toen generatieve AI-toepassingen opkwamen, heb ik regelmatig beweerd dat deze zouden kunnen leiden tot cognitive offloading. Dit zou kunnen bijdragen aan het verlagen van de werkdruk en studiebelasting. Inmiddels waarschuwen deskundigen mede op basis van onderzoek dat cognitive offloading er juist toe kan leiden dat lerenden minder effectief leren. Er moet m.i. ruimte zijn voor meer nuance in deze discussie.#edutoot #onderwijs #cognitiveoffloading #artificialintelligence
https://www.te-learning.nl/blog/is-cognitive-offloading-dankzij-ai-altijd-schadelijk-voor-leren/ -
Warum ich niemals KI nutzen würde, Sie es dennoch im Studium lernen sollten
Absolut sehenswerter Videoessay von Kevin Schumacher (KIT-Bibliothek, HoC-Schreiblabor): https://lnkd.in/dNy2C7gw
#ai #ki #llm #learning #teaching #sustainability #competence #technology #automatization #growth #bias #innovation #writing #cognitiveoffloading #illusionofcompetence #society #ethics #digitaledrecksarbeit #mentalhealth #copyright #unemployment #criticalthinking
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Warum ich niemals KI nutzen würde, Sie es dennoch im Studium lernen sollten
Absolut sehenswerter Videoessay von Kevin Schumacher (KIT-Bibliothek, HoC-Schreiblabor): https://lnkd.in/dNy2C7gw
#ai #ki #llm #learning #teaching #sustainability #competence #technology #automatization #growth #bias #innovation #writing #cognitiveoffloading #illusionofcompetence #society #ethics #digitaledrecksarbeit #mentalhealth #copyright #unemployment #criticalthinking
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Cognitive Offloading.
I like that term.
AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking
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"The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists."
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"The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists."