Assigning AI: Seven Approaches for Students, with Prompts
Open accessAbstract
This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator, and AI-student, each with distinct pedagogical benefits and risks. Prompts are included for each of these approaches. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI’s output, errors, and biases. These strategies promote active oversight, critical assessment of AI outputs, and complementarity of AI's capabilities with the students' unique insights. By challenging students to remain the "human in the loop," the authors aim to enhance learning outcomes while ensuring that AI serves as a supportive tool rather than a replacement. The proposed framework offers a guide for educators navigating the integration of AI-assisted learning in classrooms.
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Claims the paper makes
6 of 38 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Learners need to continually monitor and regulate their own thinking and learning.
“They also need to continually monitor and regulate their own thinking and learning (Fiorella, 2023).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Fiorella, 2023 source
The Metacognitive Barriers section says learners must monitor and regulate their performance when using generative learning activities. It does not establish the broader claim that learners must continually monitor and regulate all their thinking and learning; no page number is available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Premisecitation
Providing feedback at regular intervals throughout the learning journey facilitates timely course corrections, maximizing potential for improvement.
“Providing feedback at regular intervals throughout the learning journey facilitates timely course corrections, maximizing potential for improvement (Wiggins, 2015).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Wiggins, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Tutoring, particularly high-dosage tutoring, has been shown to improve learning outcomes.
“Tutoring, particularly high-dosage tutoring, has been shown to improve learning outcomes (Kraft et al, 2021).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kraft et al, 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
AI has the potential to help students get frequent feedback as they work by providing immediate and adaptive reactions to their projects.
“AI has the potential to help students get frequent feedback as they work by providing immediate and adaptive reactions to their projects.”
Quote in the paperSubstantial claim - Premisecitation
In a tutoring session, students get more opportunities to restate ideas in their own words, explain, think out loud, answer questions, and elaborate on responses than they would in a classroom.
“In a tutoring session, students get more opportunities to restate ideas in their own words, explain, think out loud, answer questions, and elaborate on responses than they would in a classroom, where time is limited and one-on-one instruction isn't possible.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Fiorella & Mayer, 2015 source
- Premisecitation
Students' ability to distill meaning, take in alternative points of view, and generalize requires a degree of self-distancing.
“Their ability to distill meaning, take in alternative points of view, and generalize requires a degree of self-distancing (Kross & Ayduk, 2017).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kross & Ayduk, 2017 source
- Premisecitation
Effective tutors enhance learning outcomes by prompting students to generate their own responses during tutoring sessions.
“Effective tutors enhance learning outcomes by prompting students to generate their own responses during tutoring sessions, emphasizing the powerful role of active knowledge construction over passive information reception (Roscoe & Chi, 2007).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Roscoe & Chi, 2007 source
- Premisecitation
Both prospective and reflective metacognitive processes teach learners to engage in mental time travel.
“Both processes teach learners to engage in”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Michaelian, 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Boucher & Scoboria, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Seligman, 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
The student's active construction or generation of new knowledge because of the interaction is critical to learning.
“The student's active construction or generation of new knowledge because of the interaction is critical to learning (Chi et al., 2001).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Chi et al., 2001 source
- Premisecitation
When feedback is coupled with practice, it creates an environment that helps students learn.
“When feedback is coupled with practice it creates an environment that helps students learn (Mccrea, 2023).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Mccrea, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
AI Language Models can potentially help students engage in metacognition, as an AI coach.
“AI Language Models can potentially help students engage in metacognition, as an AI coach.”
Quote in the paperSubstantial claim - Premisecitation
A diversity of perspectives can lead to a broader understanding of a problem and better-informed decisions.
“A diversity of perspectives can lead to a broader understanding of a problem and better-informed decisions (Haas & Mortensen, 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Haas & Mortensen, 2016
- Premisecitation
Without deep knowledge of a topic, students are unable to respond to a misunderstanding or address another's mistake.
“Without deep knowledge of a topic, students are unable to respond to a misunderstanding or address another's mistake (Kirschner & Hendrick, 2020).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kirschner & Hendrick, 2020 source
- Premisecitation
The ability to transfer skills from one context to another may depend on explicit abstraction and self-monitoring.
“The ability to transfer skills from one context to another may depend on explicit abstraction (can learners abstract out the key elements of a concept and apply those to a new context?) and self-monitoring (can learners recognize and think through how to apply a concept given a new situation?)(Salomon & Perkins, 1989).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Salomon & Perkins, 1989
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
When a student teaches someone else, they have to organize their knowledge and in doing so uncover the extent to which they understand a topic.
“When a student teaches someone else, they have to organize their knowledge and in doing so uncover the extent to which they understand a topic.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Willingham, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Teaching others is a more powerful learning technique than re-reading or summarizing.
“Teaching others is a more powerful learning technique than re-reading or summarizing.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Dunlosky et al., 2013 source
- Premisecitation
After students have built up some knowledge of a concept or series of concepts, practice can help students synthesize what they know.
“After students have built up some knowledge of a concept or series of concepts, practice can help students synthesize what they know.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Willingham, 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Students need time to write down their thoughts and think deeply about an experience.
“students need time to write down their thoughts and think deeply about an experience;”
Quote in the paperSubstantial claim - Interpretationreasoning
By practicing and making mistakes in a scenario, students can learn from their mistakes and refine their performance as they notice subtleties of a skill that weren't obvious or perhaps explicit when first learning it.
“By practicing and making mistakes in a scenario, students can learn from their mistakes and refine their performance as they notice subtleties of a skill that weren't obvious or perhaps explicit when first learning it.”
Quote in the paperSubstantial claim - Premisecitation
To generate an explanation, students must compare concepts and consider differences and similarities between concepts.
“to generate an explanation, students much compare concepts and consider differences and similarities between concepts.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Dunlosky et al., 2013 source
- Premisecitation
Teaching others helps students learn.
“Theory Teaching others helps students learn (Carey, 2015).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Carey, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Students often prefer to do rather than to take time to organize their thinking.
“and students often prefer to”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Stefano D. et al., 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
This type of practice can reduce errors when students encounter the same challenge in real life.
“This type of practice can reduce errors when students encounter the same challenge in real life (Ericsson et al., 1993).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ericsson et al., 1993 source
- Interpretationreasoning
Explaining a topic to others requires not just general familiarity but also deep expertise or fluency.
“And explaining that topic to others requires not just general familiarity but also deep expertise or fluency.”
Quote in the paperNot a substantial claim
- Classified as probably not a substantial claim. 39
- May combine two claims. 32
Classified as probably not a substantial claim. May combine two claims.
PremisecitationLearning requires motivation and self-regulation.
“Theory: Learning requires motivation and self-regulation.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Fiorella, 2023 source
The Motivational Barriers section links motivation to learners’ use of effortful, self-regulated strategies. The Metacognitive Barriers section says generative learning activities require learners to plan, monitor, and regulate their performance. These passages concern effective use of generative learning activities, not a categorical requirement that all learning requires both motivation and self-regulation; page numbers were not available. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Classified as probably not a substantial claim.
PremisecitationLearners must be motivated to put in the work to make sense of new ideas.
“Learners must be motivated to put in the work to make sense of new ideas.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites Fiorella, 2023 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationTo be effective, feedback should be timely and goal-oriented, helping students achieve their objectives.
“To be effective, that feedback should be timely and goal-oriented, helping students achieve their objectives.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Wiliam, 2011 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationEffective feedback pinpoints gaps and errors, and offers explanations about what students should do to improve.
“Effective feedback pinpoints gaps and errors, and offers explanations about what students should do to improve (Wiliam, 2011).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Wiliam, 2011 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationWe are often poor judges of our own knowledge and may overestimate our understanding of various subjects, blurring the line between familiarity and fluency.
“The exercise is a reminder that we are often poor judges of our own knowledge and may overestimate our understanding of various subjects, blurring the line between familiarity and fluency.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Deslauriers et al., 2019 source
The Results and Discussion say that novice students are poor judges of their learning and that fluent lectures can reinforce inflated perceptions of their abilities. The conclusion suggests this effect may generalize to other college-level subjects. However, the paper discusses fluency of instruction, not a distinction between familiarity with a subject and fluency in it. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenUnlike educators in a classroom, the AI doesn't know the students or understand the students' context.
“Unlike educators in classroom, it doesn't know the students or understand the students' context”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningWith guidance and oversight, some of the work of providing personalized feedback can shift to the AI.
“With guidance and oversight however, some of that work can shift to the AI.”
Quote in the paperSubstantial claim- Rests on an assumption: Ongoing, tailored feedback is difficult and time-consuming to implement in a large class setting.
May combine two claims.
PremisereasoningStudents need to frame an experience in a broader context to extract lessons from that experience and to plan ahead.
“While any experience (a test, an assignment, a simulation, a team project) is tied to the present moment, to extract lessons from that experience and to plan ahead, students need to frame the experience in a broader context.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenThe process of self-distancing can often be challenging.
“Yet, the process of self-distancing can often be challenging.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningAlthough the AI shows a lot of promise in providing effective feedback, it does not always do so.
“It's important to note that although the AI shows a lot of promise in providing effective feedback, it does not always do so.”
Quote in the paperNot a substantial claim- Rests on an assumption: Large Language Models are prone to producing incorrect but plausible facts (confabulation/hallucination).
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenYou can't know what the AI will say to any student.
“Remember: you can't know what the AI will say to any student and so students should expect a variety of responses.”
Quote in the paperNot a substantial claim- Rests on an assumption: You can't know what the AI will say to any student.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEffective feedback has three components: feed-up, feedback, and feed-forward.
“It has three components: feed-up, feedback, and feed-forward.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kirschner, & Neelen, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationMaking mistakes can help students learn, particularly if those mistakes are followed by feedback tailored to the individual student.
“Making mistakes can help students learn.particularly if those mistakes are followed by feedback tailored to the individual student (Metcalfe, 2012).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Metcalfe, 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenThe AI can make up information and is not equally adept across all domains or topics.
“Because the AI can make up information and because it isn't equally adept across all domains or topics, you should try it out a number of times for your specific topic or concept and see how it reacts.”
Quote in the paperNot a substantial claim- Rests on an assumption: Large Language Models are prone to producing incorrect but plausible facts (confabulation/hallucination).
- Rests on an assumption: The AI can make up information and isn't equally adept across all domains or topics.
Classified as probably not a substantial claim.
Premisenone givenMetacognitive exercises are crucial for learning.
“Metacognitive exercises are crucial for learning but take time and effort and are difficult to prompt in classroom settings for a number of reasons:”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenConfabulation risks for AI Mentor are manageable as long as students take the output of the AI as one possible form of feedback, rather than assuming it is correct.
“Confabulation risks for this use of AI are manageable as long as students take the output of the AI as one possible form of feedback, rather than assuming it is correct.”
Quote in the paperNot a substantial claim- Rests on an assumption: Large Language Models are prone to producing incorrect but plausible facts (confabulation/hallucination).
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationReflection after action involves learners blending new information with prior knowledge.
“This type of metacognition involves”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Schön, D., 1987
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Di Stefano et al., 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationStudents benefit from tutoring by paying close attention to a skill or topic, actively working through problems, and getting immediate feedback as they make progress.
“Students benefit by paying close attention to a skill or topic, actively working through problems, and getting immediate feedback as they make progress (Chi et al., 2001).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Chi et al., 2001 source
Probably not a substantial claim: may not pass S4 (based).
Premisenone givenMetacognition plays a pivotal role in learning, enabling students to digest, retain, and apply newfound knowledge.
“Metacognition plays a pivotal role in learning, enabling students to digest, retain, and apply newfound knowledge.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationMetacognitive exercises can help students generalize and extract meaning from an experience or simulate future scenarios.
“Metacognitive exercises can help students generalize and extract meaning from an experience or simulate future scenarios.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenOpenAI's ChatGPT may not be the best source for a recent topic because it's not connected to the internet.
“OpenAI's ChatGPT may not be the best source for your topic if it's a recent topic because it's not connected to the internet.”
Quote in the paperNot a substantial claim- Rests on an assumption: ChatGPT may not be the best source for a recent topic because it's not connected to the internet.
May combine two claims.
InterpretationreasoningThe AI can help and direct students to engage in a metacognitive process and help them articulate their thoughts about a past event or plan for the future.
“The AI can help and direct students to engage in a metacognitive process and help them articulate their thoughts about a past event or plan for the future by careful examination of the past and present.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningThe AI coach can help students reflect after an experience, a test, or a team project.
“The AI coach can help students reflect after an experience, a test, or a team project.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningFor students with knowledge of a topic, AI can be used to help generate examples and explanations and prompt them to explain a topic to the AI and clear up inaccuracies, gaps, and missing aspects of a topic.
“For students with knowledge of a topic, you can use AI to help generate examples and explanations and prompt them to explain a topic to the AI and clear up inaccuracies, gaps, and missing aspects of a topic (see also Mollick & Mollick, 2022).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Mollick & Mollick, 2022 source
Classified as probably not a substantial claim.
InterpretationreasoningThe AI can augment instruction by playing the role of scenario builder and feedback engine.
“The AI can augment instruction by playing the role of scenario builder and feedback engine.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningStudents will need to think through how to adapt their skills to various circumstances, which requires that students transfer what they learned.
“Students will need to think through how to adapt their skills to various circumstances.This requires that students transfer what they learned.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningIt can prompt individuals to recognize and balance skill sets on any team.
“It can prompt individuals to recognize and balance skill sets on any team, and it can play”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningPractice through role play can not only engage students and give them a sense of agency but help them practice and hone their skills.
“Practice through role play can not only engage students and give them a sense of agency (as they make decisions in each scenario) but help them practice and hone their skills.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningIn this scenario, the AI aids with personalized, readily available tutoring and coaching outside of the classroom and the classroom transforms into a hub of systematic engagement.
“In this scenario, the AI aids with personalized, readily available tutoring and coaching outside of the classroom and the classroom transforms into a hub of systematic engagement.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisereasoningTeammates can provide social support and different perspectives, challenging each other to question points of view and initial assumptions.
“Teammates can provide social support and, crucially, different perspectives, challenging each others to question points of view and initial assumptions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningThe AI coach can help students plan before starting any team project.
“It can also help students plan before starting any team project.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningRole-playing challenges and engages students and gives them opportunities to practice the skills they have learned in realistic scenarios.
“Role-playing challenges and engages students and gives them opportunities to practice the skills they have learned in realistic scenarios.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe process of explaining concepts to others prompts students to piece together the elements of a concept, explicitly name those elements, and organize their knowledge so that it can be readily articulated.
“The process of explaining concepts to others calls prompts students to piece together the elements of a concept, explicitly name those elements, and organize their knowledge so that it can be readily articulated (Willingham, 2023).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Willingham, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningBy asking students to explicitly name what the AI gets wrong (or right) and teach the AI the concept, the prompt challenges student understanding of a topic and questions their assumptions about the depth of their knowledge.
“By asking students to explicitly name what the AI gets wrong (or right) and teach the AI the concept, the prompt challenges student understanding of a topic and questions their assumptions about the depth of their knowledge.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningSuch changes foster an active learning environment, inviting each student to engage with class concepts, articulate reasoning, and actively construct knowledge.
“Such changes foster an active learning environment, inviting each student to engage with class concepts, articulate reasoning, and actively construct knowledge.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningThe student's assessment of the AI's output and their suggestions for improvement of that output is a learning opportunity.
“The student's assessment of the AI's output and their suggestions for improvement of that output is a learning opportunity.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationTeaching someone else requires flexible knowledge and the ability to improvise responses.
“Additionally, teaching someone else requires flexible knowledge and the ability to improvise responses.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Kirschner & Hendrick, 2020 source
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningIt can play devil's advocate helping teams question their underlying assumptions and providing alternative viewpoints for any decision.
“It can prompt individuals to recognize and balance skill sets on any team, and it can play”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationnone givenOnly by learning to use these new tools firsthand can students begin to understand their implications, and prepare themselves for a world where AI forms an important part of their lives.
“Only by learning to use these new tools firsthand can students begin to understand their implications, and prepare themselves for a world where AI forms an important part of their lives.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationMetacognitive exercises take time and effort and are difficult to prompt in classroom settings.
“Metacognitive exercises are crucial for learning but take time and effort and are difficult to prompt in classroom settings for a number of reasons:”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningAs students role play, they may encounter different scenarios that call for adaptation of theory.
“As students role play, they may encounter different scenarios that call for adaptation of theory.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationnone givenAI tools have the potential to aid independent learning and potentially offer personalized engagement.
“As these tools have the potential to aid independent learning and potentially offer personalized engagement, educators can experiment with devoting more in-class time to active discussions, question-and-answer sessions, and collaborative exercises such as 'think-pair-share' sessions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningAI can play a role in helping teams articulate and think through these issues.
“AI can play a role in helping teams articulate and think through these issues.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWhen tasked with conveying an idea to another, the complexities and intricacies previously overlooked are revealed.
“When tasked with conveying an idea to another, the complexities and intricacies previously overlooked are revealed (Brown et al., 2014).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Brown et al., 2014 source
May combine two claims.
PremisecitationTeams can outperform individuals working alone on many tasks, but only if team members leverage each other's strengths and focus on dividing tasks based on skills and expertise.
“Teams can outperform individuals working alone on many tasks, but only if team members leverage each other's strengths and focus on dividing tasks based on skills and expertise (Hackman, 2011).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hackman, 2011
May combine two claims.
PremisecitationThis kind of practice activates hard thinking as students are pushed out of their comfort zone and are asked to apply theory to practice.
“This kind of practice activates hard thinking as students are pushed out of their comfort zone and are asked to apply theory to practice (Bjork & Bjork, 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Bjork & Bjork, 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationRole-playing is an effective way to challenge students to think about the skills that they have learned; it can serve as a form of deliberate practice.
“Role-playing is an effective way to challenge students to think about the skills that they have learned; it can serve as a form of deliberate practice, or activity aimed at improving the current level of performance (Ericsson & Pool, 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ericsson & Pool, 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningAI has the potential to help teams increase their collaborative intelligence.
“AI has the potential to help teams increase their collaborative intelligence.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningIt can act as a teammate worthy of a seat at the table, and which can be consulted before making decisions to inspire new action.
“Similarly, it can act as a”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenAI has the potential to help students practice hard to practice skills in new situations.
“AI has the potential to help students practice hard to practice skills in new situations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThe explanation uncovers gaps in understanding and underscores what students understand and what they don't understand or can't fully explain.
“The explanation uncovers gaps in understanding and underscores what students understand and what they don't understand or can't fully explain.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
PremisereasoningAvoiding groupthink and harnessing team members' expertise for projects require a concerted effort.
“Avoiding groupthink and harnessing team members' expertise for projects require a concerted effort”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
PremisecitationSelf-change is hard and engaging in a re-examination of past events in order to plan for the future is an effortful process.
“self-change is hard and engaging in a re-examination of past events in order to plan for the future is an effortful process;”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Ongoing, tailored feedback is difficult and time-consuming to implement in a large class setting.
“While ongoing, tailored feedback is important, it is difficult and time-consuming to implement in a large class setting.”
The paper relies on this without support.
Large Language Models are prone to producing incorrect but plausible facts (confabulation/hallucination).
“Large Language Models are prone to producing incorrect, but plausible facts, a phenomenon known as confabulation or hallucination.”
The paper relies on this without support.
AI errors can be deeply woven into outputs and hard to detect.
“These errors can be deeply woven into the outputs of the AI, and can be hard to detect.”
The paper relies on this without support.
Different LLMs have different rates of confabulation errors; GPT-4 and Bing have the lowest error rates; errors are most common when asking for quotes, sources, citations, or detailed information.
“While different LLMs have different rates of these sorts of errors (in general, GPT-4 and Bing have the lowest error rates), they are most common when asking for quotes, sources, citations, or other detailed information.”
The paper relies on this without support.
AI training processes may introduce biases ranging from gender and racial biases to biases against particular viewpoints, approaches, or political affiliations.
“Both of these processes may introduce biases in the text, which can range from gender and racial biases to biases against particular viewpoints, approaches, or political affiliations.”
The paper relies on this without support.
Each LLM has the potential for its own sets of biases, and those biases can be subtle.
“Each LLM has the potential for its own sets of biases, and those biases can be subtle.”
The paper relies on this without support.
When data is entered into AI, it can be used for future training by the organizations developing the AI.
“When data is entered into the AI, it can be used for future training by the organizations developing the AI.”
The paper relies on this without support.
The current state of privacy remains unclear for many models, and legal implications are often uncertain.
“While ChatGPT offers a privacy mode that claims to not use input for future AI training, the current state of privacy remains unclear for many models, and the legal implications are often also uncertain.”
The paper relies on this without support.
There is a substantial risk that students will use AI as a crutch, undermining learning.
“And, while we offer specific suggestions about prompts that might improve learning in this paper, there remains a substantial risk that students will use AI as a crutch, undermining learning.”
The paper relies on this without support.
The AI can get stuck in a loop.
“It can also get stuck in a loop.”
The paper relies on this without support.
It is very easy to imbue meaning into AI responses.
“It's very easy to imbue meaning into AI responses”
The paper relies on this without support.
Many users seem to use AI tutoring.
“Despite these risks, many users seem to use AI tutoring.”
The paper relies on this without support.
The AI can make up information and isn't equally adept across all domains or topics.
“Because the AI can make up information and because it isn't equally adept across all domains or topics”
The paper relies on this without support.
ChatGPT may not be the best source for a recent topic because it's not connected to the internet.
“OpenAI's ChatGPT may not be the best source for your topic if it's a recent topic because it's not connected to the internet.”
The paper relies on this without support.
You can't know what the AI will say to any student.
“Remember: you can't know what the AI will say to any student”
The paper relies on this without support.
AI outputs are unpredictable and based on statistical models.
“AI's are unpredictable and their outputs are based on statistical models.”
The paper relies on this without support.
In general, the AI will remain helpful given its instructions.
“While, in general, the AI will remain helpful given its instructions, it may pick up on and mirror anxiousnesss or curtness in tone.”
The paper relies on this without support.
The AI may pick up on and mirror anxiousness or curtness in tone.
“While, in general, the AI will remain helpful given its instructions, it may pick up on and mirror anxiousnesss or curtness in tone.”
The paper relies on this without support.
Students interacting with the AI as a coach may find that the AI refuses to work with them or gets into a loop and cannot recall the next step.
“Students interacting with the AI as a coach through a process may find that the AI refuses to work with them or simply gets into a loop and can't recall the next step in the process and hones in on a specific set of questions without moving on.”
The paper relies on this without support.
The AI is not a real person responding to the student.
“It's not a coach, but it may feel like one.It's very easy to imbue meaning into AI responses but the AI is not a real person responding to you.”
The paper relies on this without support.
The AI does not know the student or their context.
“It is capable of a lot, but it doesn't know you or your context.”
The paper relies on this without support.
The AI can get stuck in a series of questions that are unrelated to the exercise.
“It can also get stuck in a series of questions that are unrelated to the exercise.”
The paper relies on this without support.
The AI can hallucinate or make things up.
“It can make”
The paper relies on this without support.
Anything the student shares may be used as training data for the AI.
“Anything you share may be used as training data for the AI.”
The paper relies on this without support.
The AI as teammate may react to the student's tone or style.
“The AI as teammate may react to your tone or style.”
The paper relies on this without support.
If a student argues with the AI, it may decide that the student wants it to argue back, and adopt a confrontational tone.
“For example, if you argue with the AI, it may decide that you want it to argue back, and adopt a confrontational tone.”
The paper relies on this without support.
The AI can confabulate or make up facts that lead teams to the wrong decision, though this risk is only moderate given that the AI is mostly designed to spark debate.
“The AI can confabulate or make up facts that lead teams to the wrong decision, though this risk is only moderate given that the AI is mostly designed to spark debate.”
The paper relies on this without support.
The AI can argue with the team, particularly Microsoft's Bing in Creative Mode.
“It can”
The paper relies on this without support.
The AI can give teams advice that is not specific or contextualized.
“It can”
The paper relies on this without support.
Students who begin to think of the AI as a teammate may not challenge its opinions or advice and may be tempted to let the AI take the lead, even when it is less than helpful.
“As social norms may dictate that we don't explicitly challenge teammates, students who begin to think of the AI as a teammate may not challenge its opinions or advice and may be tempted to let the AI take the lead, even when it's less than helpful.”
The paper relies on this without support.
AI can hallucinate or make up facts.
“The AI can hallucinate and make up facts about any concept.”
The paper relies on this without support.
AI may simply refuse the prompt.
“The AI may simply refuse the prompt (in which case students should try again or try a different Large Language Model)”
The paper relies on this without support.
AI may not recognize or understand the examples students want or may argue with students about their critique.
“it may not recognize or understand the examples the students want or it may argue with students about their critique.”
The paper relies on this without support.
Students may not know enough about the topic to assess the AI's output effectively and may not feel confident enough to push back.
“students may not know enough about the topic to assess the AIs output effectively and may not feel confident enough to push back, should the AI disagree with their assessment.”
The paper relies on this without support.
Large Language Models behave differently every time you give them a prompt.
“Note that Large Language Models will not only behave differently every time you give them a prompt, there are differences between them”
The paper relies on this without support.
ChatGPT4 and Microsoft's Bing in Creative Mode tend to be more accurate than ChatGPT3.5 but not in all cases.
“ChatGTP4 and Microsoft's Bing in Creative Mode tend to be more accurate than ChaptGPT3.5 but not in all cases.”
The paper relies on this without support.
Microsoft's Bing in Creative Mode may argue or quibble if corrected.
“Additionally, Microsoft's Bing in Creative Mode may argue or quibble if corrected.”
The paper relies on this without support.
The AI is not a real person and doesn't know you or your context.
“It's not a person, but it may feel like one.The AI is not a real person responding to you.It is capable of a lot, but it doesn't know you or your context.”
The paper relies on this without support.
OpenAI's ChatGPT is not connected to the internet and doesn't have knowledge beyond 2021.
“OpenAI's ChatGPT is not connected to the internet and doesn't have knowledge beyond 2021”
The paper relies on this without support.
AI can produce a new situation for every student multiple times and play specific roles in that scenario.
“The AI can produce a new situation for every student multiple times and play specific roles in that scenario.”
The paper relies on this without support.
Working with individual students on different scenarios and responding to each student separately is intensely time-consuming and difficult for any instructor.
“In any classroom working with individual students on different scenarios and responding to each student separately is intensely time-consuming and difficult for any instructor.”
The paper relies on this without support.
No scenario will be effective for all students.
“Additionally, no scenario will be effective for all students.”
The paper relies on this without support.
Students may focus on elements of the story and lose track of the general concept; students may not be familiar with elements, causing extraneous load on working memory.
“Because the AI is creating a story, students may focus on elements of the story and lose track of the general concept; additionally, students may not be familiar with elements of the story, causing extraneous load on their working memory”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
LLMs can effectively act as tutor, mentor or coach, by merely using sophisticated prompting techniques.
“In education contexts it has been shown that LLMs can effectively act as tutor, mentor or coach, by merely using sophisticated prompting techniques (83).”
From The benefits and dangers of anthropomorphic conversational agents (2025) · cites this paper as [83]
The “AI as Tutor: Providing Direct Instruction” section says an AI tutor can be invoked with simple prompting, and the “AI as Mentor: Example Prompt” and “AI as Coach: Guidelines for Instructors” sections describe prompts for those roles. But “LLMs: Prompts and Risks” says these educational uses are largely untested, so the paper does not establish that LLMs can effectively perform all three roles. (LLM, conf=0.90)
Experimental — accuracy not yet measuredFaculty training is equally critical: educators must be equipped to integrate AI responsibly into their teaching practices, guide student use, and assess its impact on learning and academic culture.
“At the same time, faculty training is equally critical: educators must be equipped to integrate AI responsibly into their teaching practices, guide student use, and assess its impact on learning and academic culture [45,46].”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [46]
Such strategies reposition AI as a learning ally rather than a means of circumvention.
“Such strategies reposition AI as a learning ally rather than a means of circumvention [46].”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [46]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Model-compatibility claim conflicts with the appendix
MinorThe text says every prompt works with every LLM, but the compatibility chart marks several prompt-model combinations "no."
“Also note, that all prompts work for all LLMs.”
“Increasing Knowledge AI-Tutor yes no yes no no”
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Exploring critical AI literacy in language education: A case study
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The Integration of Generative AI Tools in Academic Writing: Implications for Student Research
Rodafinos, A. (2025) · Social Education Research · doi:10.37256/ser.6220256517 · Cited by 11 · see citing works
Hau, M. F. (2024) · Acta Sociologica · doi:10.1177/00016993241264152 · Cited by 9 · see citing works
Using Generative AI to Promote the APA's Five Goals for Undergraduate Majors
Fortner, M., & Katzarska-Miller, I. I. (2024) · Teaching of Psychology · doi:10.1177/00986283241264793 · Cited by 8 · see citing works
Borderline Disaster: An Empirical Study on Student Usage of GenAI in a Law Assignment
Alimardani, A. (2025) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2025.3540978 · Cited by 9 · see citing works
When Efficiency Meets Fulfillment: Understanding Long-Term LLM Integration in Knowledge Work
Kobiella, C., Mitrevska, T., Schmidt, A., & Draxler, F. (2025) · Symposium on Human-Computer Interaction for Work · doi:10.1145/3729176.3729180 · Cited by 14 · see citing works
Jang, E.-S., & Oh, K. (2025) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2025.28.7.008 · Cited by 7 · see citing works
Generative AI use and self-learning in higher education: the role of learning difficulties
Saleh, D., & ElSayary, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2025.1737928 · Cited by 13 · see citing works
Ravarini, A., Canavesi, A., & Passerini, K. (2024) · International Conference on Higher Education Advances (HEAd) · doi:10.4995/head24.2024.17345 · Cited by 7 · see citing works
Wang, B., Zhang, C., Khamo, & Ye, S. (2024) · IFLA Journal · doi:10.1177/03400352241291905 · Cited by 8 · see citing works
ChatGPT in Computer Science Education: A Case Study on a Database Administration Course
López-Fernández, D., & Vergaz, R. (2025) · Applied Sciences · doi:10.3390/app15020985 · Cited by 13 · see citing works
Zhao, Z., Li, J., Hong, Y., & Yun, T. Y. (2025) · International Journal for Lesson and Learning Studies · doi:10.1108/ijlls-01-2025-0029 · Cited by 8 · see citing works
Artificial intelligence as a mentor in the graduate online classroom: opportunities and challenges
Kellam, H., Cortés, L. P., & Gilmore, T. (2025) · Artificial Intelligence in Education · doi:10.1108/aiie-10-2024-0034 · Cited by 8 · see citing works
Zhang, Y., & Samsudin, M. A. (2026) · Education Sciences · doi:10.3390/educsci16010150 · Cited by 10 · see citing works
Leveling Up or Leveling Down? The Impact of Generative AI on Student Performance in Business Schools
Bergenholtz, C., Vuculescu, O., Günzel-Jensen, F., & Frederiksen, L. (2026) · Academy of Management Learning and Education · doi:10.5465/amle.2025.0029 · Cited by 6 · see citing works
Perla, L., & Vinci, V. (2024) · Qwerty · doi:10.30557/qw000079 · Cited by 6 · see citing works
Li, M., Belpoliti, F., Taha, G., & Zhang, M. (2025) · Foreign Language Annals · doi:10.1111/flan.70041 · Cited by 9 · see citing works
The Grounding Problem: An Approach to the Integration of Cognitive and Generative Models
Maher, M. L., Ventura, D. A., & Magerko, B. S. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v2i1.27695 · Cited by 5 · see citing works
Empowering STEM Education With AI and Prompt Engineering
Ragab, K. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7332-3.ch011 · Cited by 5 · see citing works
Algorithmic governance and AI: balancing innovation and oversight in Indonesian policy analyst
Kusumasari, B., & Yahya, B. N. (2024) · AI & Society · doi:10.1007/s00146-024-02002-1 · Cited by 6 · see citing works
Pratschke, B. M. (2024) · Springer briefs in education · doi:10.1007/978-3-031-67991-9_4 · Cited by 5 · see citing works
Mastery learning in CS1: a longitudinal study during and post-pandemic
Buraphadeja, V., & Srisarkun, V. (2024) · Discover Education · doi:10.1007/s44217-024-00361-x · Cited by 5 · see citing works
Teaching Nursing Students Effective Artificial Intelligence Prompt Engineering
Bester, M. E., & Zeigler, K. (2025) · Nurse Educator · doi:10.1097/nne.0000000000001969 · Cited by 4 · see citing works
Normative Influence on the Adoption of Generative Artificial Intelligence in Higher Education
Pierce, M. E., & Jiang, P. (2025) · Journal of Marketing Education · doi:10.1177/02734753251397827 · Cited by 4 · see citing works
He, X., Zhou, R., Fan, Q., Xiao, X., Yu, Y., & Yan, Z. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3557037 · Cited by 7 · see citing works
Tutor or Mentor. A Comparative Usability Study of AI Roles in Higher Education
Marconi, L., & Cabitza, F. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-99267-4_25 · Cited by 4 · see citing works
Promoting Critical Consciousness in Students through Artifacts Co-creation with GAI
Behboudi, A., Adefisayo, A. O., & Irgens, G. A. (2024) · Symposium on Learning, Design and Technology (LDT) · doi:10.1145/3663433.3663435 · Cited by 4 · see citing works
Early Adoption of Generative AI by Global Business Leaders: Insights from an INSEAD Alumni SurveyPreprint
Davis, J. P., & Li, J. B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4786119 · Cited by 4 · see citing works
Bahmani, A. H., Cha, K., Alavi, A., Dixit, A., Ross, A., Park, R. M., Goncalves, F., Ma, S., Saxman, P., Nair, R. V., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01034-y · Cited by 7 · see citing works
Enhancing Cultural Competence in Nursing Education Through Generative Artificial Intelligence Tools
Şan, E. Ö., & Lim, F. A. (2025) · Journal of Transcultural Nursing · doi:10.1177/10436596251372944 · Cited by 3 · see citing works
AI-Informed Pedagogy for a Post-Truth Era
Nally, D. (2025) · Digital Society · doi:10.1007/s44206-025-00230-6 · Cited by 3 · see citing works
ChatGPT-4o as an automated scoring tool for writing assessment: Strengths and weaknesses
Karaçeper, R. D., & Kıray, G. (2026) · International Journal of Assessment Tools in Education · doi:10.21449/ijate.1701871 · Cited by 5 · see citing works
Teaching with ai in marketing education: the leo framework as a pedagogical guide
Vollrath, M. D., & Villegas, S. G. (2026) · Marketing Education Review · doi:10.1080/10528008.2026.2623032 · Cited by 4 · see citing works
Day, M. J. (2026) · Journal of ICT and Education · doi:10.69739/jicte.v1i1.1626 · Cited by 3 · see citing works
The Role of AI in Programming Education
Mahir, A., Shohel, M. M. C., & Sall, W. (2024) · Advances in higher education and professional development book series · doi:10.4018/979-8-3693-1499-9.ch012 · Cited by 3 · see citing works
Educación Inteligente para el Siglo XXI
Almazán-López, O., & Osuna-Acedo, S. (2024) · VISUAL REVIEW International Visual Culture Review / Revista Internacional de Cultura Visual · doi:10.62161/revvisual.v16.5395 · Cited by 5 · see citing works
Moreno, C. (2025) · South Florida Journal of Development · doi:10.46932/sfjdv6n5-001 · Cited by 3 · see citing works
Inteligencia artificial con perspectiva humanista
Contreras-Ruiz, M. A., Carrasco, I. O., & Rebolledo, C. C. (2024) · DIDAC · doi:10.48102/didac.2024..84_jul-dic.219 · Cited by 2 · see citing works
PLAID: Supporting Computing Instructors to Identify Domain-Specific Programming Plans at Scale
Jain, Y., Demirtaş, M. A., & Cunningham, K. I. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713832 · Cited by 2 · see citing works
Qadir, J. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016479 · Cited by 12 · see citing works
Pozdniakov, S., Brazil, J. R., Poquet, O., Krusche, S., Berrezueta-Guzmán, J., Sadiq, S. W., & Khosravi, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98465-5_37 · Cited by 2 · see citing works
Thompson, K. N., Chandler, K. L., Morgan, C., Khashabi, D., Delinski, E. A., & Durme, B. V. (2025) · Journal of Advanced Academics · doi:10.1177/1932202x251356323 · Cited by 5 · see citing works
Pamungkas, R., Suwono, H., & Ibrohim, I. (2025) · Salud Ciencia y Tecnología · doi:10.56294/saludcyt20251976 · Cited by 2 · see citing works
Nabhani, F. A., Hamzah, M., & Abuhassna, H. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-00232-7_53 · Cited by 3 · see citing works
Marconi, L., Brasca, F., & Ferri, P. M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13187-4_14 · Cited by 3 · see citing works
Verma, A., & Singh, P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3000-6.ch013 · Cited by 2 · see citing works
Graduate attributes in the age of AI: suggestions for purposeful uses of ChatGPT in higher education
Comerio, G. (2026) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-11-2024-0268 · Cited by 2 · see citing works
Revolutionizing Education: The Era of Large Language Models
Choi, H.-W., & Abdirayimov, S. (2024) · Journal of Multimedia Information System · doi:10.33851/jmis.2024.11.1.97 · Cited by 4 · see citing works
Análise de ferramentas para geração de avatares artificiais a partir de pessoas reais
Verhalen, L. E. C., Castro, M. M. M., & Maciel, C. D. O. (2024) · doi:10.5753/erigo.2024.4798 · Cited by 2 · see citing works
Córdoba, U. N. D. R. C., Chiecher, A. C., & Argentina (2025) · Praxis Educativa · doi:10.19137/praxiseducativa-2025-290204 · Cited by 2 · see citing works
- Authors
- Mollick, E. R., & Mollick, L.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Mollick, Ethan R.
- ORCID
- Mollick, Ethan R.
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2023
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4381546750
- Cited by
- 319
- References
- 29
- Citations by year
- 2026: 172 · 2025: 81 · 2024: 59 · 2023: 6 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- large language models, artificial intelligence in education, AI-assisted learning, human-in-the-loop, AI tutors, AI coach, AI teammates, algorithmic bias, AI errors, teaching methods
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-11.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-11.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
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What counts as a claim ›How we measure confidence ›How we check this ›
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