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#teachertraining — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #teachertraining, aggregated by home.social.

  1. If “punishment” is part of your pedagogy, you should just walk away quietly from work as an educator, and if you don’t, then you should be dragged away making whatever noise you will. #education #teacher #teachertraining #edchat #highereducation #highschool

  2. If “punishment” is part of your pedagogy, you should just walk away quietly from work as an educator, and if you don’t, then you should be dragged away making whatever noise you will. #education #teacher #teachertraining #edchat #highereducation #highschool

  3. If “punishment” is part of your pedagogy, you should just walk away quietly from work as an educator, and if you don’t, then you should be dragged away making whatever noise you will. #education #teacher #teachertraining #edchat #highereducation #highschool

  4. If “punishment” is part of your pedagogy, you should just walk away quietly from work as an educator, and if you don’t, then you should be dragged away making whatever noise you will. #education #teacher #teachertraining #edchat #highereducation #highschool

  5. Another step towards AI-ready schools.

    We conducted an AI Workshop at Global Discovery School, Rampura Phul, empowering educators with practical AI for the classroom.

    #Codju #AIInEducation #TeacherTraining

  6. Empowering teachers with AI.

    We conducted an AI Workshop at Delhi Public School, Patiala, enabling educators to explore practical AI tools and strategies for future-ready classrooms.

    #Codju #AIEducation #TeacherTraining #AIInEducation #FutureReadySchools

  7. Another step towards AI-ready schools.

    Proud to conduct a CBSE CT & AI Expert-Led Workshop at Gobindgarh Public School, Mandi Gobindgarh, empowering educators with practical AI and Computational Thinking.

    #Codju #TeacherTraining #CBSE #AIEducation

  8. DATE: June 28, 2026 at 10:00AM
    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: Teachers say they distrust AI but still accept its harsh grading mistakes, study finds

    URL: psypost.org/teachers-say-they-

    As artificial intelligence becomes more common in professional settings, human oversight is often promoted as a safeguard against automated mistakes. A new study published in PNAS Nexus suggests that human experts are significantly more likely to accept incorrectly harsh decisions when they believe those decisions come from an artificial intelligence system rather than a human colleague. This pattern provides evidence that relying on human review to catch algorithmic errors might not be as effective as many expect.

    Study author Rigissa Megalokonomou, an associate professor of economics at Monash University, was drawn to the issue because of the rapid integration of new technology in classrooms. “My research is in the economics of education, so pressing topics in education naturally fall within my interests and right now, AI is perhaps the most pressing of all,” she said. Grading decisions offer a realistic way to observe how experts respond to flawed advice in a high-stakes environment.

    “Grading is one of the most consequential moments in a student’s school life, shaping their futures and self-perception as learners,” Megalokonomou explained. “When AI enters that process and introduces errors that professionals aren’t catching, that’s a serious problem worth studying.” Automated grading tools promise to save time and offer consistent scoring, but they can still make mistakes or introduce bias.

    When organizations use automated tools to help make decisions, humans are generally expected to review the output and catch any mistakes. This expectation assumes that people can objectively evaluate a computer program’s suggestion and correct it when it strays from the truth. Ensuring that these oversight mechanisms work well is necessary to realize the benefits of automated technology without compromising accuracy or public trust.

    Teachers are expected to act as a safety net to spot and fix errors, acting as the final decision-makers in the classroom. Research in this area explores how users interact with automated advice, looking at the psychological factors that drive acceptance or rejection. Sometimes people distrust computer programs entirely, a concept known as algorithm aversion. In other situations, individuals might blindly trust automated outputs over human judgment, often called automation bias.

    The authors wanted to understand what exactly makes an expert correct a machine’s mistake or let it pass without intervention. “The standard reassurance around AI is ‘don’t worry, a human will check its work.’ Our study tests whether that actually holds up,” Megalokonomou noted. To answer this question, the researchers conducted a preregistered randomized experiment involving active teachers in Greece.

    “We ran an experiment with over 1,300 teachers in Greece, randomly assigning them to grade student work paired with a deliberately wrong score labeled as coming from either an AI system or a human colleague,” Megalokonomou said. “We then measured how far their final grade strayed from the objectively correct answer.” The participants taught various subjects, including mathematics, science, and the humanities.

    During the study, each teacher reviewed a sample of student work that matched their specific area of expertise. The student work was accompanied by a bulleted checklist showing exactly which parts of the answer were correct or incorrect. Along with the student’s answers, the teachers saw a preassigned score of five out of ten, which was intentionally incorrect.

    The researchers manipulated the direction of the grading error for different groups of participants. In one scenario, the score of five out of ten was too harsh because the student’s work actually deserved an eight based on the objective checklist. In the second scenario, the same score was too lenient because the student only provided enough correct answers to earn a two.

    After reviewing the work and the suggested score, the teachers assigned their own final grade. The main measurement in the study was the grading fairness gap. This metric calculates the absolute mathematical distance between the teacher’s final grade and the objectively correct benchmark grade. A larger gap indicates that the teacher failed to correct the initial flawed recommendation.

    “We found that when AI gave a harsh grade, one that was too low, teachers were significantly less likely to correct it than when the same wrong grade came from a human colleague,” Megalokonomou told PsyPost. “The grading fairness gap was 22% larger for harsh AI errors.” Teachers tended to accept the stricter automated grade and leave it largely uncorrected. When the identical harsh grade came from a human colleague, the teachers were more willing to fix the mistake and boost the student’s score.

    In the lenient scenario, the source of the recommendation did not make a statistical difference. Teachers corrected the overly generous grades equally well, regardless of whether they thought a machine or a human made the error. They did not show the same deference to the computer program when it gave a student too much credit. This provides evidence that the credibility of algorithmic grading depends heavily on the direction of the recommendation.

    The scientists also asked the participants to rate the original grader on five psychological dimensions to understand their thought process. These dimensions included perceived ability, comprehension of the subject, fairness, good intent, and responsibility. The answers helped explain why teachers responded differently to the harsh and lenient computer errors.

    Megalokonomou highlighted a major contradiction in the survey responses. The most surprising finding was “the gap between what teachers said about AI and how they actually behaved,” she noted. “They rated AI as less fair, less competent, and less accountable than a human colleague, and most said they didn’t want to use it.”

    Despite those negative views, behavior shifted when grading real work. “Yet when the AI gave a harsh grade, they deferred to it more than they did to a human making the identical error,” Megalokonomou explained. “Distrust didn’t make them more vigilant; if anything, it went the other way.”

    In the harsh scenario, teachers perceived the algorithm as having high technical ability and responsibility. This perception of competence motivated the educators to accept the strict grade. The harshness itself appeared to function as a signal that the computer program was rigorous and capable. Higher perceived ability and responsibility explained over half of the effect in the harsh scenario.

    In the lenient scenario, teachers viewed the artificial intelligence much more negatively across all five psychological dimensions. Because they felt the lenient algorithm lacked competence, fairness, and good intent, they actively rejected its advice. They stepped in to correct the inflated score and return the grade to its fair level. Unless the algorithm scored well on all these traits, the teachers overrode its lenient advice.

    The researchers also looked at how different demographic groups reacted. “Strikingly, this pattern was most pronounced among younger, more educated, and more tech-confident teachers, exactly the people we would expect to be the most critical users of AI,” Megalokonomou said. Because these groups are often viewed as early adopters of new technology, this finding challenges the common belief that tech savvy professionals automatically provide stronger oversight.

    Humanities teachers also showed a slightly higher tendency to defer to the machine than science and math teachers did. The researchers suggest that algorithmic advice might become more influential when evaluation criteria are highly subjective. At the end of the survey, the researchers also asked the teachers about their general attitudes toward artificial intelligence. Nearly half of the respondents reported using generative artificial intelligence tools at least weekly for lesson preparation.

    Despite using these tools for planning, the teachers remained skeptical about delegating actual evaluative authority to machines. In open text responses, many educators voiced concerns about a computer’s inability to account for individual student circumstances. They pointed out that human grading often requires empathy and context, such as understanding a student’s learning difficulties or family issues. This suggests that practices relying solely on improving an algorithm’s technical accuracy are unlikely to overcome teachers’ ethical objections.

    But there are a few limitations. “The experiment was conducted with teachers in Greece, so readers should be cautious about generalizing directly to other national contexts or professional settings,” Megalokonomou noted. The way these specific teachers interact with technology might not perfectly reflect the behavior of professionals in other countries or cultural environments.

    “The study was also designed around a specific, controlled scenario, a single grading task with a deliberately wrong score, which allowed us to isolate the effect cleanly, but real-world grading involves more complexity and repeated interactions with AI tools over time,” she added. The experimental design made the correct grade relatively easy to figure out using a straightforward checklist. In real classroom environments, grading is often more ambiguous and takes place under severe time pressure.

    Real world ambiguity could either increase a person’s reliance on algorithmic advice or prompt stronger independent judgment. Future research could explore whether this deference to harsh automated judgments extends to other evaluative tasks, such as formative assessments or hiring decisions. Scientists might also vary the amount of explanation the computer program provides to see if detailed rationales prompt humans to look closer at the results.

    The researchers hope to apply these insights to help improve professional practices. “I am already working on a teacher training program focused specifically on AI oversight: not just how to use AI tools, but how to recognize when your own judgment is likely to go astray,” Megalokonomou shared. “I hope this research reaches the policymakers and school leaders making decisions about AI in education right now.”

    “The question of whether human oversight actually works tends to get assumed rather than tested,” she said. The findings offer a strong warning that treating humans as an automatic safeguard is insufficient.

    “One thing I want readers to sit with is the broader implication. This study is about teachers and grading, but the dynamic we uncovered, where human oversight breaks down selectively depending on what the AI is doing, applies well beyond education,” Megalokonomou emphasized. As automated tools become common worldwide, these insights offer a useful starting point for understanding how experts interact with machines.

    “Any setting where AI recommendations are paired with human review, whether that is healthcare, hiring, or criminal justice, faces the same underlying challenge,” she warned. “Putting a human in the loop is not enough on its own. If we want meaningful oversight, we need to design it deliberately, with structured checks and clear accountability mechanisms, rather than assuming good intentions will be sufficient.”

    The study, “Why do experts miss AI’s errors? Evidence from a randomized labeling experiment,” was authored by Sofoklis Goulas, Rigissa Megalokonomou, and Panagiotis Sotirakopoulos.

    URL: psypost.org/teachers-say-they-

    -------------------------------------------------

    Private, vetted email list for mental health professionals: 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 #AIinEducation #GradingBias #AlgorithmAversion #AutomationBias #AIEthics #TeacherTraining #OversightInAI #EducationResearch #PNASNexus #AIGradingStudy

  9. DATE: June 28, 2026 at 10:00AM
    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: Teachers say they distrust AI but still accept its harsh grading mistakes, study finds

    URL: psypost.org/teachers-say-they-

    As artificial intelligence becomes more common in professional settings, human oversight is often promoted as a safeguard against automated mistakes. A new study published in PNAS Nexus suggests that human experts are significantly more likely to accept incorrectly harsh decisions when they believe those decisions come from an artificial intelligence system rather than a human colleague. This pattern provides evidence that relying on human review to catch algorithmic errors might not be as effective as many expect.

    Study author Rigissa Megalokonomou, an associate professor of economics at Monash University, was drawn to the issue because of the rapid integration of new technology in classrooms. “My research is in the economics of education, so pressing topics in education naturally fall within my interests and right now, AI is perhaps the most pressing of all,” she said. Grading decisions offer a realistic way to observe how experts respond to flawed advice in a high-stakes environment.

    “Grading is one of the most consequential moments in a student’s school life, shaping their futures and self-perception as learners,” Megalokonomou explained. “When AI enters that process and introduces errors that professionals aren’t catching, that’s a serious problem worth studying.” Automated grading tools promise to save time and offer consistent scoring, but they can still make mistakes or introduce bias.

    When organizations use automated tools to help make decisions, humans are generally expected to review the output and catch any mistakes. This expectation assumes that people can objectively evaluate a computer program’s suggestion and correct it when it strays from the truth. Ensuring that these oversight mechanisms work well is necessary to realize the benefits of automated technology without compromising accuracy or public trust.

    Teachers are expected to act as a safety net to spot and fix errors, acting as the final decision-makers in the classroom. Research in this area explores how users interact with automated advice, looking at the psychological factors that drive acceptance or rejection. Sometimes people distrust computer programs entirely, a concept known as algorithm aversion. In other situations, individuals might blindly trust automated outputs over human judgment, often called automation bias.

    The authors wanted to understand what exactly makes an expert correct a machine’s mistake or let it pass without intervention. “The standard reassurance around AI is ‘don’t worry, a human will check its work.’ Our study tests whether that actually holds up,” Megalokonomou noted. To answer this question, the researchers conducted a preregistered randomized experiment involving active teachers in Greece.

    “We ran an experiment with over 1,300 teachers in Greece, randomly assigning them to grade student work paired with a deliberately wrong score labeled as coming from either an AI system or a human colleague,” Megalokonomou said. “We then measured how far their final grade strayed from the objectively correct answer.” The participants taught various subjects, including mathematics, science, and the humanities.

    During the study, each teacher reviewed a sample of student work that matched their specific area of expertise. The student work was accompanied by a bulleted checklist showing exactly which parts of the answer were correct or incorrect. Along with the student’s answers, the teachers saw a preassigned score of five out of ten, which was intentionally incorrect.

    The researchers manipulated the direction of the grading error for different groups of participants. In one scenario, the score of five out of ten was too harsh because the student’s work actually deserved an eight based on the objective checklist. In the second scenario, the same score was too lenient because the student only provided enough correct answers to earn a two.

    After reviewing the work and the suggested score, the teachers assigned their own final grade. The main measurement in the study was the grading fairness gap. This metric calculates the absolute mathematical distance between the teacher’s final grade and the objectively correct benchmark grade. A larger gap indicates that the teacher failed to correct the initial flawed recommendation.

    “We found that when AI gave a harsh grade, one that was too low, teachers were significantly less likely to correct it than when the same wrong grade came from a human colleague,” Megalokonomou told PsyPost. “The grading fairness gap was 22% larger for harsh AI errors.” Teachers tended to accept the stricter automated grade and leave it largely uncorrected. When the identical harsh grade came from a human colleague, the teachers were more willing to fix the mistake and boost the student’s score.

    In the lenient scenario, the source of the recommendation did not make a statistical difference. Teachers corrected the overly generous grades equally well, regardless of whether they thought a machine or a human made the error. They did not show the same deference to the computer program when it gave a student too much credit. This provides evidence that the credibility of algorithmic grading depends heavily on the direction of the recommendation.

    The scientists also asked the participants to rate the original grader on five psychological dimensions to understand their thought process. These dimensions included perceived ability, comprehension of the subject, fairness, good intent, and responsibility. The answers helped explain why teachers responded differently to the harsh and lenient computer errors.

    Megalokonomou highlighted a major contradiction in the survey responses. The most surprising finding was “the gap between what teachers said about AI and how they actually behaved,” she noted. “They rated AI as less fair, less competent, and less accountable than a human colleague, and most said they didn’t want to use it.”

    Despite those negative views, behavior shifted when grading real work. “Yet when the AI gave a harsh grade, they deferred to it more than they did to a human making the identical error,” Megalokonomou explained. “Distrust didn’t make them more vigilant; if anything, it went the other way.”

    In the harsh scenario, teachers perceived the algorithm as having high technical ability and responsibility. This perception of competence motivated the educators to accept the strict grade. The harshness itself appeared to function as a signal that the computer program was rigorous and capable. Higher perceived ability and responsibility explained over half of the effect in the harsh scenario.

    In the lenient scenario, teachers viewed the artificial intelligence much more negatively across all five psychological dimensions. Because they felt the lenient algorithm lacked competence, fairness, and good intent, they actively rejected its advice. They stepped in to correct the inflated score and return the grade to its fair level. Unless the algorithm scored well on all these traits, the teachers overrode its lenient advice.

    The researchers also looked at how different demographic groups reacted. “Strikingly, this pattern was most pronounced among younger, more educated, and more tech-confident teachers, exactly the people we would expect to be the most critical users of AI,” Megalokonomou said. Because these groups are often viewed as early adopters of new technology, this finding challenges the common belief that tech savvy professionals automatically provide stronger oversight.

    Humanities teachers also showed a slightly higher tendency to defer to the machine than science and math teachers did. The researchers suggest that algorithmic advice might become more influential when evaluation criteria are highly subjective. At the end of the survey, the researchers also asked the teachers about their general attitudes toward artificial intelligence. Nearly half of the respondents reported using generative artificial intelligence tools at least weekly for lesson preparation.

    Despite using these tools for planning, the teachers remained skeptical about delegating actual evaluative authority to machines. In open text responses, many educators voiced concerns about a computer’s inability to account for individual student circumstances. They pointed out that human grading often requires empathy and context, such as understanding a student’s learning difficulties or family issues. This suggests that practices relying solely on improving an algorithm’s technical accuracy are unlikely to overcome teachers’ ethical objections.

    But there are a few limitations. “The experiment was conducted with teachers in Greece, so readers should be cautious about generalizing directly to other national contexts or professional settings,” Megalokonomou noted. The way these specific teachers interact with technology might not perfectly reflect the behavior of professionals in other countries or cultural environments.

    “The study was also designed around a specific, controlled scenario, a single grading task with a deliberately wrong score, which allowed us to isolate the effect cleanly, but real-world grading involves more complexity and repeated interactions with AI tools over time,” she added. The experimental design made the correct grade relatively easy to figure out using a straightforward checklist. In real classroom environments, grading is often more ambiguous and takes place under severe time pressure.

    Real world ambiguity could either increase a person’s reliance on algorithmic advice or prompt stronger independent judgment. Future research could explore whether this deference to harsh automated judgments extends to other evaluative tasks, such as formative assessments or hiring decisions. Scientists might also vary the amount of explanation the computer program provides to see if detailed rationales prompt humans to look closer at the results.

    The researchers hope to apply these insights to help improve professional practices. “I am already working on a teacher training program focused specifically on AI oversight: not just how to use AI tools, but how to recognize when your own judgment is likely to go astray,” Megalokonomou shared. “I hope this research reaches the policymakers and school leaders making decisions about AI in education right now.”

    “The question of whether human oversight actually works tends to get assumed rather than tested,” she said. The findings offer a strong warning that treating humans as an automatic safeguard is insufficient.

    “One thing I want readers to sit with is the broader implication. This study is about teachers and grading, but the dynamic we uncovered, where human oversight breaks down selectively depending on what the AI is doing, applies well beyond education,” Megalokonomou emphasized. As automated tools become common worldwide, these insights offer a useful starting point for understanding how experts interact with machines.

    “Any setting where AI recommendations are paired with human review, whether that is healthcare, hiring, or criminal justice, faces the same underlying challenge,” she warned. “Putting a human in the loop is not enough on its own. If we want meaningful oversight, we need to design it deliberately, with structured checks and clear accountability mechanisms, rather than assuming good intentions will be sufficient.”

    The study, “Why do experts miss AI’s errors? Evidence from a randomized labeling experiment,” was authored by Sofoklis Goulas, Rigissa Megalokonomou, and Panagiotis Sotirakopoulos.

    URL: psypost.org/teachers-say-they-

    -------------------------------------------------

    Private, vetted email list for mental health professionals: 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 #AIinEducation #GradingBias #AlgorithmAversion #AutomationBias #AIEthics #TeacherTraining #OversightInAI #EducationResearch #PNASNexus #AIGradingStudy

  10. DATE: June 28, 2026 at 10:00AM
    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: Teachers say they distrust AI but still accept its harsh grading mistakes, study finds

    URL: psypost.org/teachers-say-they-

    As artificial intelligence becomes more common in professional settings, human oversight is often promoted as a safeguard against automated mistakes. A new study published in PNAS Nexus suggests that human experts are significantly more likely to accept incorrectly harsh decisions when they believe those decisions come from an artificial intelligence system rather than a human colleague. This pattern provides evidence that relying on human review to catch algorithmic errors might not be as effective as many expect.

    Study author Rigissa Megalokonomou, an associate professor of economics at Monash University, was drawn to the issue because of the rapid integration of new technology in classrooms. “My research is in the economics of education, so pressing topics in education naturally fall within my interests and right now, AI is perhaps the most pressing of all,” she said. Grading decisions offer a realistic way to observe how experts respond to flawed advice in a high-stakes environment.

    “Grading is one of the most consequential moments in a student’s school life, shaping their futures and self-perception as learners,” Megalokonomou explained. “When AI enters that process and introduces errors that professionals aren’t catching, that’s a serious problem worth studying.” Automated grading tools promise to save time and offer consistent scoring, but they can still make mistakes or introduce bias.

    When organizations use automated tools to help make decisions, humans are generally expected to review the output and catch any mistakes. This expectation assumes that people can objectively evaluate a computer program’s suggestion and correct it when it strays from the truth. Ensuring that these oversight mechanisms work well is necessary to realize the benefits of automated technology without compromising accuracy or public trust.

    Teachers are expected to act as a safety net to spot and fix errors, acting as the final decision-makers in the classroom. Research in this area explores how users interact with automated advice, looking at the psychological factors that drive acceptance or rejection. Sometimes people distrust computer programs entirely, a concept known as algorithm aversion. In other situations, individuals might blindly trust automated outputs over human judgment, often called automation bias.

    The authors wanted to understand what exactly makes an expert correct a machine’s mistake or let it pass without intervention. “The standard reassurance around AI is ‘don’t worry, a human will check its work.’ Our study tests whether that actually holds up,” Megalokonomou noted. To answer this question, the researchers conducted a preregistered randomized experiment involving active teachers in Greece.

    “We ran an experiment with over 1,300 teachers in Greece, randomly assigning them to grade student work paired with a deliberately wrong score labeled as coming from either an AI system or a human colleague,” Megalokonomou said. “We then measured how far their final grade strayed from the objectively correct answer.” The participants taught various subjects, including mathematics, science, and the humanities.

    During the study, each teacher reviewed a sample of student work that matched their specific area of expertise. The student work was accompanied by a bulleted checklist showing exactly which parts of the answer were correct or incorrect. Along with the student’s answers, the teachers saw a preassigned score of five out of ten, which was intentionally incorrect.

    The researchers manipulated the direction of the grading error for different groups of participants. In one scenario, the score of five out of ten was too harsh because the student’s work actually deserved an eight based on the objective checklist. In the second scenario, the same score was too lenient because the student only provided enough correct answers to earn a two.

    After reviewing the work and the suggested score, the teachers assigned their own final grade. The main measurement in the study was the grading fairness gap. This metric calculates the absolute mathematical distance between the teacher’s final grade and the objectively correct benchmark grade. A larger gap indicates that the teacher failed to correct the initial flawed recommendation.

    “We found that when AI gave a harsh grade, one that was too low, teachers were significantly less likely to correct it than when the same wrong grade came from a human colleague,” Megalokonomou told PsyPost. “The grading fairness gap was 22% larger for harsh AI errors.” Teachers tended to accept the stricter automated grade and leave it largely uncorrected. When the identical harsh grade came from a human colleague, the teachers were more willing to fix the mistake and boost the student’s score.

    In the lenient scenario, the source of the recommendation did not make a statistical difference. Teachers corrected the overly generous grades equally well, regardless of whether they thought a machine or a human made the error. They did not show the same deference to the computer program when it gave a student too much credit. This provides evidence that the credibility of algorithmic grading depends heavily on the direction of the recommendation.

    The scientists also asked the participants to rate the original grader on five psychological dimensions to understand their thought process. These dimensions included perceived ability, comprehension of the subject, fairness, good intent, and responsibility. The answers helped explain why teachers responded differently to the harsh and lenient computer errors.

    Megalokonomou highlighted a major contradiction in the survey responses. The most surprising finding was “the gap between what teachers said about AI and how they actually behaved,” she noted. “They rated AI as less fair, less competent, and less accountable than a human colleague, and most said they didn’t want to use it.”

    Despite those negative views, behavior shifted when grading real work. “Yet when the AI gave a harsh grade, they deferred to it more than they did to a human making the identical error,” Megalokonomou explained. “Distrust didn’t make them more vigilant; if anything, it went the other way.”

    In the harsh scenario, teachers perceived the algorithm as having high technical ability and responsibility. This perception of competence motivated the educators to accept the strict grade. The harshness itself appeared to function as a signal that the computer program was rigorous and capable. Higher perceived ability and responsibility explained over half of the effect in the harsh scenario.

    In the lenient scenario, teachers viewed the artificial intelligence much more negatively across all five psychological dimensions. Because they felt the lenient algorithm lacked competence, fairness, and good intent, they actively rejected its advice. They stepped in to correct the inflated score and return the grade to its fair level. Unless the algorithm scored well on all these traits, the teachers overrode its lenient advice.

    The researchers also looked at how different demographic groups reacted. “Strikingly, this pattern was most pronounced among younger, more educated, and more tech-confident teachers, exactly the people we would expect to be the most critical users of AI,” Megalokonomou said. Because these groups are often viewed as early adopters of new technology, this finding challenges the common belief that tech savvy professionals automatically provide stronger oversight.

    Humanities teachers also showed a slightly higher tendency to defer to the machine than science and math teachers did. The researchers suggest that algorithmic advice might become more influential when evaluation criteria are highly subjective. At the end of the survey, the researchers also asked the teachers about their general attitudes toward artificial intelligence. Nearly half of the respondents reported using generative artificial intelligence tools at least weekly for lesson preparation.

    Despite using these tools for planning, the teachers remained skeptical about delegating actual evaluative authority to machines. In open text responses, many educators voiced concerns about a computer’s inability to account for individual student circumstances. They pointed out that human grading often requires empathy and context, such as understanding a student’s learning difficulties or family issues. This suggests that practices relying solely on improving an algorithm’s technical accuracy are unlikely to overcome teachers’ ethical objections.

    But there are a few limitations. “The experiment was conducted with teachers in Greece, so readers should be cautious about generalizing directly to other national contexts or professional settings,” Megalokonomou noted. The way these specific teachers interact with technology might not perfectly reflect the behavior of professionals in other countries or cultural environments.

    “The study was also designed around a specific, controlled scenario, a single grading task with a deliberately wrong score, which allowed us to isolate the effect cleanly, but real-world grading involves more complexity and repeated interactions with AI tools over time,” she added. The experimental design made the correct grade relatively easy to figure out using a straightforward checklist. In real classroom environments, grading is often more ambiguous and takes place under severe time pressure.

    Real world ambiguity could either increase a person’s reliance on algorithmic advice or prompt stronger independent judgment. Future research could explore whether this deference to harsh automated judgments extends to other evaluative tasks, such as formative assessments or hiring decisions. Scientists might also vary the amount of explanation the computer program provides to see if detailed rationales prompt humans to look closer at the results.

    The researchers hope to apply these insights to help improve professional practices. “I am already working on a teacher training program focused specifically on AI oversight: not just how to use AI tools, but how to recognize when your own judgment is likely to go astray,” Megalokonomou shared. “I hope this research reaches the policymakers and school leaders making decisions about AI in education right now.”

    “The question of whether human oversight actually works tends to get assumed rather than tested,” she said. The findings offer a strong warning that treating humans as an automatic safeguard is insufficient.

    “One thing I want readers to sit with is the broader implication. This study is about teachers and grading, but the dynamic we uncovered, where human oversight breaks down selectively depending on what the AI is doing, applies well beyond education,” Megalokonomou emphasized. As automated tools become common worldwide, these insights offer a useful starting point for understanding how experts interact with machines.

    “Any setting where AI recommendations are paired with human review, whether that is healthcare, hiring, or criminal justice, faces the same underlying challenge,” she warned. “Putting a human in the loop is not enough on its own. If we want meaningful oversight, we need to design it deliberately, with structured checks and clear accountability mechanisms, rather than assuming good intentions will be sufficient.”

    The study, “Why do experts miss AI’s errors? Evidence from a randomized labeling experiment,” was authored by Sofoklis Goulas, Rigissa Megalokonomou, and Panagiotis Sotirakopoulos.

    URL: psypost.org/teachers-say-they-

    -------------------------------------------------

    Private, vetted email list for mental health professionals: 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 #AIinEducation #GradingBias #AlgorithmAversion #AutomationBias #AIEthics #TeacherTraining #OversightInAI #EducationResearch #PNASNexus #AIGradingStudy

  11. Pulmuone Foundation Trains Teachers in ‘Empathy Education’

    At Part 1 of the “Empathy Education Teacher Training Program” held at Gangdong Central Library in Gangdong-gu, Seoul…
    #EuropeSays #Korea #KR #Seoul #empathyeducation #Gangdong-SongpaOfficeofEducation #Koreaeducation #PulmuoneFoundation #Student-TailoredIntegratedSupportAct #sustainabledietaryeducation #teachertraining
    europesays.com/korea/63931/

  12. STEM Teaching with Embedded Primary Sources Workshops

    In addition to an asynchronous STEPS workshop that runs through November 30, the Center for Mathematics and Science Education, University of Mississippi is also offering two onsite day-long workshops July 16 in Greenwood, MS, and July 20 in Blue Springs, MS.

    Onsite STEPS workshop registration deadline: July 8

    olemiss.edu/cmse/professional-

    #BlueSprings #Misssissippi #StemEducation #TeacherTraining #TeacherProfssionalDevelopment #Education

  13. STEM Teaching with Embedded Primary Sources Workshops

    In addition to an asynchronous STEPS workshop that runs through November 30, the Center for Mathematics and Science Education, University of Mississippi is also offering two onsite day-long workshops July 16 in Greenwood, MS, and July 20 in Blue Springs, MS.

    Onsite STEPS workshop registration deadline: July 8

    olemiss.edu/cmse/professional-

    #BlueSprings #Misssissippi #StemEducation #TeacherTraining #TeacherProfssionalDevelopment #Education

  14. STEM Teaching with Embedded Primary Sources Workshops

    In addition to an asynchronous STEPS workshop that runs through November 30, the Center for Mathematics and Science Education, University of Mississippi is also offering two onsite day-long workshops July 16 in Greenwood, MS, and July 20 in Blue Springs, MS.

    Onsite STEPS workshop registration deadline: July 8

    olemiss.edu/cmse/professional-

    #BlueSprings #Misssissippi #StemEducation #TeacherTraining #TeacherProfssionalDevelopment #Education

  15. STEM Teaching with Embedded Primary Sources Workshops

    In addition to an asynchronous STEPS workshop that runs through November 30, the Center for Mathematics and Science Education, University of Mississippi is also offering two onsite day-long workshops July 16 in Greenwood, MS, and July 20 in Blue Springs, MS.

    Onsite STEPS workshop registration deadline: July 8

    olemiss.edu/cmse/professional-

    #BlueSprings #Misssissippi #StemEducation #TeacherTraining #TeacherProfssionalDevelopment #Education

  16. STEM Teaching with Embedded Primary Sources Workshops

    In addition to an asynchronous STEPS workshop that runs through November 30, the Center for Mathematics and Science Education, University of Mississippi is also offering two onsite day-long workshops July 16 in Greenwood, MS, and July 20 in Blue Springs, MS.

    Onsite STEPS workshop registration deadline: July 8

    olemiss.edu/cmse/professional-

    #BlueSprings #Misssissippi #StemEducation #TeacherTraining #TeacherProfssionalDevelopment #Education

  17. Library of Congress Comics Cohorts

    The National Council of Teachers of English (NCTE) is seeking 15 current or recent ELA educators, including classroom teachers of grades 4-12, community college teachers, and preservice teacher educators to participate in this Teaching with Primary Sources (TPS) project. Selected applicants will convene in small teams, starting this summer and continuing through December, to create resources that integrate comics with works of literature and build reading comprehension, empathy, and critical thinking. Participants, who must obtain NCTE membership, will receive a stipend of $1,500 for their contributions. | Cohort information & application deadline: June 24

    Learn more: ncte.org/ela-comics-cohorts-ap

    #Education #Teaching #EnglishTeacher #LanguageArts #EnglishLanguageArts #Comics #TeachingWithPrimarySources #TPS #TeacherTraining #TeacherProfessionalDevelopment

  18. Library of Congress Comics Cohorts

    The National Council of Teachers of English (NCTE) is seeking 15 current or recent ELA educators, including classroom teachers of grades 4-12, community college teachers, and preservice teacher educators to participate in this Teaching with Primary Sources (TPS) project. Selected applicants will convene in small teams, starting this summer and continuing through December, to create resources that integrate comics with works of literature and build reading comprehension, empathy, and critical thinking. Participants, who must obtain NCTE membership, will receive a stipend of $1,500 for their contributions. | Cohort information & application deadline: June 24

    Learn more: ncte.org/ela-comics-cohorts-ap

    #Education #Teaching #EnglishTeacher #LanguageArts #EnglishLanguageArts #Comics #TeachingWithPrimarySources #TPS #TeacherTraining #TeacherProfessionalDevelopment

  19. Library of Congress Comics Cohorts

    The National Council of Teachers of English (NCTE) is seeking 15 current or recent ELA educators, including classroom teachers of grades 4-12, community college teachers, and preservice teacher educators to participate in this Teaching with Primary Sources (TPS) project. Selected applicants will convene in small teams, starting this summer and continuing through December, to create resources that integrate comics with works of literature and build reading comprehension, empathy, and critical thinking. Participants, who must obtain NCTE membership, will receive a stipend of $1,500 for their contributions. | Cohort information & application deadline: June 24

    Learn more: ncte.org/ela-comics-cohorts-ap

    #Education #Teaching #EnglishTeacher #LanguageArts #EnglishLanguageArts #Comics #TeachingWithPrimarySources #TPS #TeacherTraining #TeacherProfessionalDevelopment

  20. Library of Congress Comics Cohorts

    The National Council of Teachers of English (NCTE) is seeking 15 current or recent ELA educators, including classroom teachers of grades 4-12, community college teachers, and preservice teacher educators to participate in this Teaching with Primary Sources (TPS) project. Selected applicants will convene in small teams, starting this summer and continuing through December, to create resources that integrate comics with works of literature and build reading comprehension, empathy, and critical thinking. Participants, who must obtain NCTE membership, will receive a stipend of $1,500 for their contributions. | Cohort information & application deadline: June 24

    Learn more: ncte.org/ela-comics-cohorts-ap

    #Education #Teaching #EnglishTeacher #LanguageArts #EnglishLanguageArts #Comics #TeachingWithPrimarySources #TPS #TeacherTraining #TeacherProfessionalDevelopment

  21. Library of Congress Comics Cohorts

    The National Council of Teachers of English (NCTE) is seeking 15 current or recent ELA educators, including classroom teachers of grades 4-12, community college teachers, and preservice teacher educators to participate in this Teaching with Primary Sources (TPS) project. Selected applicants will convene in small teams, starting this summer and continuing through December, to create resources that integrate comics with works of literature and build reading comprehension, empathy, and critical thinking. Participants, who must obtain NCTE membership, will receive a stipend of $1,500 for their contributions. | Cohort information & application deadline: June 24

    Learn more: ncte.org/ela-comics-cohorts-ap

    #Education #Teaching #EnglishTeacher #LanguageArts #EnglishLanguageArts #Comics #TeachingWithPrimarySources #TPS #TeacherTraining #TeacherProfessionalDevelopment

  22. Can your students tell the difference between fact, opinion, misinformation and AI-generated content? 🔍🤖

    Join the News Literacy Project on July 21 from 11 AM–12 PM EDT and discover practical ways to teach news literacy across subjects. Learn how to help students evaluate sources, assess credibility, build healthy media habits and become informed participants in our democracy.

    This free, live, online webinar lasts one hour and grants professional development credit (for US teachers).

    Learn more & register now:

    sharemylesson.com/webinars/mis

    #NoAI #Education #Teachers #Webinar #MediaLiteracy #DigitalLiteracy #ProfessionalDevelopment #Parenting #TeacherTraining #Homeschooling

  23. Can your students tell the difference between fact, opinion, misinformation and AI-generated content? 🔍🤖

    Join the News Literacy Project on July 21 from 11 AM–12 PM EDT and discover practical ways to teach news literacy across subjects. Learn how to help students evaluate sources, assess credibility, build healthy media habits and become informed participants in our democracy.

    This free, live, online webinar lasts one hour and grants professional development credit (for US teachers).

    Learn more & register now:

    sharemylesson.com/webinars/mis

    #NoAI #Education #Teachers #Webinar #MediaLiteracy #DigitalLiteracy #ProfessionalDevelopment #Parenting #TeacherTraining #Homeschooling

  24. Can your students tell the difference between fact, opinion, misinformation and AI-generated content? 🔍🤖

    Join the News Literacy Project on July 21 from 11 AM–12 PM EDT and discover practical ways to teach news literacy across subjects. Learn how to help students evaluate sources, assess credibility, build healthy media habits and become informed participants in our democracy.

    This free, live, online webinar lasts one hour and grants professional development credit (for US teachers).

    Learn more & register now:

    sharemylesson.com/webinars/mis

    #NoAI #Education #Teachers #Webinar #MediaLiteracy #DigitalLiteracy #ProfessionalDevelopment #Parenting #TeacherTraining #Homeschooling

  25. Can your students tell the difference between fact, opinion, misinformation and AI-generated content? 🔍🤖

    Join the News Literacy Project on July 21 from 11 AM–12 PM EDT and discover practical ways to teach news literacy across subjects. Learn how to help students evaluate sources, assess credibility, build healthy media habits and become informed participants in our democracy.

    This free, live, online webinar lasts one hour and grants professional development credit (for US teachers).

    Learn more & register now:

    sharemylesson.com/webinars/mis

    #NoAI #Education #Teachers #Webinar #MediaLiteracy #DigitalLiteracy #ProfessionalDevelopment #Parenting #TeacherTraining #Homeschooling

  26. Can your students tell the difference between fact, opinion, misinformation and AI-generated content? 🔍🤖

    Join the News Literacy Project on July 21 from 11 AM–12 PM EDT and discover practical ways to teach news literacy across subjects. Learn how to help students evaluate sources, assess credibility, build healthy media habits and become informed participants in our democracy.

    This free, live, online webinar lasts one hour and grants professional development credit (for US teachers).

    Learn more & register now:

    sharemylesson.com/webinars/mis

    #NoAI #Education #Teachers #Webinar #MediaLiteracy #DigitalLiteracy #ProfessionalDevelopment #Parenting #TeacherTraining #Homeschooling

  27. Level up your Summer Learning! Join us on July 22, 2026 9-10 AM PT / 12:00-1:00 PM ET for our Experiential SEL Strategies Webinar as part of Share My Lesson’s free, for-credit Summer of Learning webinar series.

    Learn about and experience multimedia activities to reduce student anxiety, encourage self-reflection & self-regulation, & foster student social emotional understanding.

    sharemylesson.com/webinars/exp

    #ProfessionalDevelopment #TeacherTraining #Education #Webinar #SocialEmotionalLearning

  28. Level up your Summer Learning! Join us on July 22, 2026 9-10 AM PT / 12:00-1:00 PM ET for our Experiential SEL Strategies Webinar as part of Share My Lesson’s free, for-credit Summer of Learning webinar series.

    Learn about and experience multimedia activities to reduce student anxiety, encourage self-reflection & self-regulation, & foster student social emotional understanding.

    sharemylesson.com/webinars/exp

    #ProfessionalDevelopment #TeacherTraining #Education #Webinar #SocialEmotionalLearning

  29. Level up your Summer Learning! Join us on July 22, 2026 9-10 AM PT / 12:00-1:00 PM ET for our Experiential SEL Strategies Webinar as part of Share My Lesson’s free, for-credit Summer of Learning webinar series.

    Learn about and experience multimedia activities to reduce student anxiety, encourage self-reflection & self-regulation, & foster student social emotional understanding.

    sharemylesson.com/webinars/exp

    #ProfessionalDevelopment #TeacherTraining #Education #Webinar #SocialEmotionalLearning

  30. Level up your Summer Learning! Join us on July 22, 2026 9-10 AM PT / 12:00-1:00 PM ET for our Experiential SEL Strategies Webinar as part of Share My Lesson’s free, for-credit Summer of Learning webinar series.

    Learn about and experience multimedia activities to reduce student anxiety, encourage self-reflection & self-regulation, & foster student social emotional understanding.

    sharemylesson.com/webinars/exp

    #ProfessionalDevelopment #TeacherTraining #Education #Webinar #SocialEmotionalLearning

  31. Level up your Summer Learning! Join us on July 22, 2026 9-10 AM PT / 12:00-1:00 PM ET for our Experiential SEL Strategies Webinar as part of Share My Lesson’s free, for-credit Summer of Learning webinar series.

    Learn about and experience multimedia activities to reduce student anxiety, encourage self-reflection & self-regulation, & foster student social emotional understanding.

    sharemylesson.com/webinars/exp

    #ProfessionalDevelopment #TeacherTraining #Education #Webinar #SocialEmotionalLearning

  32. Our June newsletter is in inboxes now! Get the scoop on our free summer learning options with Share My Lesson & the Library of Congress Teaching with Primary Sources program.

    ☀ Free & online
    ☀ Classroom-ready inspiration
    ☀ Level Up for the school year ahead

    Plus, get timely June & July teaching ideas, including a brand new teaching resource that's perfect for Juneteenth, and also Military History & World Cup Fever!

    Read it online now: us13.campaign-archive.com/?u=b

    @histodons @militaryhistory

    #Movies #Education #TeacherTraining #ProfessionalDevelopment #Webinar #Juneteenth #BlackHistory #Histodons #History #MilitaryHistory #DDay #FifaWorldCup #FIFA #WorldCup

  33. Our June newsletter is in inboxes now! Get the scoop on our free summer learning options with Share My Lesson & the Library of Congress Teaching with Primary Sources program.

    ☀ Free & online
    ☀ Classroom-ready inspiration
    ☀ Level Up for the school year ahead

    Plus, get timely June & July teaching ideas, including a brand new teaching resource that's perfect for Juneteenth, and also Military History & World Cup Fever!

    Read it online now: us13.campaign-archive.com/?u=b

    @histodons @militaryhistory

    #Movies #Education #TeacherTraining #ProfessionalDevelopment #Webinar #Juneteenth #BlackHistory #Histodons #History #MilitaryHistory #DDay #FifaWorldCup #FIFA #WorldCup

  34. Our June newsletter is in inboxes now! Get the scoop on our free summer learning options with Share My Lesson & the Library of Congress Teaching with Primary Sources program.

    ☀ Free & online
    ☀ Classroom-ready inspiration
    ☀ Level Up for the school year ahead

    Plus, get timely June & July teaching ideas, including a brand new teaching resource that's perfect for Juneteenth, and also Military History & World Cup Fever!

    Read it online now: us13.campaign-archive.com/?u=b

    @histodons @militaryhistory

    #Movies #Education #TeacherTraining #ProfessionalDevelopment #Webinar #Juneteenth #BlackHistory #Histodons #History #MilitaryHistory #DDay #FifaWorldCup #FIFA #WorldCup

  35. Our June newsletter is in inboxes now! Get the scoop on our free summer learning options with Share My Lesson & the Library of Congress Teaching with Primary Sources program.

    ☀ Free & online
    ☀ Classroom-ready inspiration
    ☀ Level Up for the school year ahead

    Plus, get timely June & July teaching ideas, including a brand new teaching resource that's perfect for Juneteenth, and also Military History & World Cup Fever!

    Read it online now: us13.campaign-archive.com/?u=b

    @histodons @militaryhistory

    #Movies #Education #TeacherTraining #ProfessionalDevelopment #Webinar #Juneteenth #BlackHistory #Histodons #History #MilitaryHistory #DDay #FifaWorldCup #FIFA #WorldCup

  36. Our June newsletter is in inboxes now! Get the scoop on our free summer learning options with Share My Lesson & the Library of Congress Teaching with Primary Sources program.

    ☀ Free & online
    ☀ Classroom-ready inspiration
    ☀ Level Up for the school year ahead

    Plus, get timely June & July teaching ideas, including a brand new teaching resource that's perfect for Juneteenth, and also Military History & World Cup Fever!

    Read it online now: us13.campaign-archive.com/?u=b

    @histodons @militaryhistory

    #Movies #Education #TeacherTraining #ProfessionalDevelopment #Webinar #Juneteenth #BlackHistory #Histodons #History #MilitaryHistory #DDay #FifaWorldCup #FIFA #WorldCup

  37. These were the words of one educator after completing our ComeThinkAgain pilot focus groups.

    It's also a reminder of why this work matters. Teachers across Europe are expected to prepare learners for a digital, green, entrepreneurial future, often without ever having been prepared for it themselves.

    As part of our piloting efforts, we're testing early micro-modules with real teachers and VET trainers

    The future of European education is being built right now. 🚀
    #GreenSkills #TeacherTraining

  38. 🍎 To celebrate Teacher Appreciation Week, we're highlighting North Putnam, the award-winning documentary about what it means to be an educator in America today.

    North Putnam depicts a year in the life of a rural school district and the community it serves. The film doesn't shy away from depicting the challenges faced by modern education, but is also is primarily a story of hope. Audiences come away feeling energized to develop effective strategies for their own communities and inspired by the administrators, teachers, students and community members of North Putnam, Indiana. This film is excellent for educator discussions and professional development! 📚🎬

    The North Putnam Study Guide is designed for grades 7-12, adult/higher ed and teacher PD.

    Learn more: journeysinfilm.org/film/north-

    #TeacherAppreciationWeek #TeacherAppreciationDay #Education #Edutooters #School #SchoolAdministrators #TeacherTraining #Movies #Documentary #Indiana #Midwest #NorthPutnam #Teachers #Teaching

  39. 🍎 To celebrate Teacher Appreciation Week, we're highlighting North Putnam, the award-winning documentary about what it means to be an educator in America today.

    North Putnam depicts a year in the life of a rural school district and the community it serves. The film doesn't shy away from depicting the challenges faced by modern education, but is also is primarily a story of hope. Audiences come away feeling energized to develop effective strategies for their own communities and inspired by the administrators, teachers, students and community members of North Putnam, Indiana. This film is excellent for educator discussions and professional development! 📚🎬

    The North Putnam Study Guide is designed for grades 7-12, adult/higher ed and teacher PD.

    Learn more: journeysinfilm.org/film/north-

    #TeacherAppreciationWeek #TeacherAppreciationDay #Education #Edutooters #School #SchoolAdministrators #TeacherTraining #Movies #Documentary #Indiana #Midwest #NorthPutnam #Teachers #Teaching

  40. 🍎 To celebrate Teacher Appreciation Week, we're highlighting North Putnam, the award-winning documentary about what it means to be an educator in America today.

    North Putnam depicts a year in the life of a rural school district and the community it serves. The film doesn't shy away from depicting the challenges faced by modern education, but is also is primarily a story of hope. Audiences come away feeling energized to develop effective strategies for their own communities and inspired by the administrators, teachers, students and community members of North Putnam, Indiana. This film is excellent for educator discussions and professional development! 📚🎬

    The North Putnam Study Guide is designed for grades 7-12, adult/higher ed and teacher PD.

    Learn more: journeysinfilm.org/film/north-

    #TeacherAppreciationWeek #TeacherAppreciationDay #Education #Edutooters #School #SchoolAdministrators #TeacherTraining #Movies #Documentary #Indiana #Midwest #NorthPutnam #Teachers #Teaching

  41. 🍎 To celebrate Teacher Appreciation Week, we're highlighting North Putnam, the award-winning documentary about what it means to be an educator in America today.

    North Putnam depicts a year in the life of a rural school district and the community it serves. The film doesn't shy away from depicting the challenges faced by modern education, but is also is primarily a story of hope. Audiences come away feeling energized to develop effective strategies for their own communities and inspired by the administrators, teachers, students and community members of North Putnam, Indiana. This film is excellent for educator discussions and professional development! 📚🎬

    The North Putnam Study Guide is designed for grades 7-12, adult/higher ed and teacher PD.

    Learn more: journeysinfilm.org/film/north-

    #TeacherAppreciationWeek #TeacherAppreciationDay #Education #Edutooters #School #SchoolAdministrators #TeacherTraining #Movies #Documentary #Indiana #Midwest #NorthPutnam #Teachers #Teaching

  42. 🍎 To celebrate Teacher Appreciation Week, we're highlighting North Putnam, the award-winning documentary about what it means to be an educator in America today.

    North Putnam depicts a year in the life of a rural school district and the community it serves. The film doesn't shy away from depicting the challenges faced by modern education, but is also is primarily a story of hope. Audiences come away feeling energized to develop effective strategies for their own communities and inspired by the administrators, teachers, students and community members of North Putnam, Indiana. This film is excellent for educator discussions and professional development! 📚🎬

    The North Putnam Study Guide is designed for grades 7-12, adult/higher ed and teacher PD.

    Learn more: journeysinfilm.org/film/north-

    #TeacherAppreciationWeek #TeacherAppreciationDay #Education #Edutooters #School #SchoolAdministrators #TeacherTraining #Movies #Documentary #Indiana #Midwest #NorthPutnam #Teachers #Teaching

  43. Looking for the Best College Of D.El.Ed in Mirzapur? 🎓
    Mahatma Degree College offers quality teacher training, experienced faculty, modern classrooms, and practical learning to help you build a successful career in education.

    Start your journey toward becoming a skilled educator today!

    madcl.in/btc.php

    #BestCollege #DElEd #Mirzapur #MahatmaDegreeCollege #TeacherTraining #Education #CareerGrowth #CollegeAdmissions #StudyInIndia

  44. On Wednesday, April 8th, 2026 instructor Wolf is hosting "Knife Safety for Children: Teaching Whittling with Confidence, Not Fear on teaching" at the Forest School Support Hub. skool.com/forestschool
    #forestschool #outdoorlife #bushcraft #survival #TeacherTraining

  45. On Wednesday, April 8th, 2026 instructor Wolf is hosting "Knife Safety for Children: Teaching Whittling with Confidence, Not Fear on teaching" at the Forest School Support Hub. skool.com/forestschool
    #forestschool #outdoorlife #bushcraft #survival #TeacherTraining