Student perceptions of ChatGPT use in a college essay assignment: Implications for learning, grading, and trust in artificial intelligence
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This study examined student experiences before and after an essay writing assignment that required the use of ChatGPT within an undergraduate engineering course. Utilizing a pre-post study design, we gathered data from 24 participants to evaluate ChatGPT's support for both completing and grading an essay assignment, exploring its educational value and impact on the learning process. Our quantitative and thematic analyses uncovered that ChatGPT did not simplify the writing process. Instead, the tool transformed the student learning experience yielding mixed responses. Participants reported finding ChatGPT valuable for learning, and their comfort with its ethical and benevolent aspects increased post-use. Concerns with ChatGPT included poor accuracy and limited feedback on the confidence of its output. Students preferred instructors to use ChatGPT to help grade their assignments, with appropriate oversight. They did not trust ChatGPT to grade by itself. Student views of ChatGPT evolved from a perceived “cheating tool” to a collaborative resource that requires human oversight and calibrated trust. Implications for writing, education, and trust in AI are discussed.
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Student-centred AI feedback mechanisms involve tasks like reflection prompts, AI-use reports, or annotated submissions, encouraging students to critically evaluate how and why they used generative AI tools.
“Student-centred AI feedback mechanisms involve tasks like reflection prompts, AI-use reports, or annotated submissions, encouraging students to critically evaluate how and why they used generative AI tools (Rawas, 2024; Tossell et al., 2024).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Tossell et al., 2024
The Assignment section (page unavailable) describes drafts that identify ChatGPT-generated content and require students to highlight and address AI errors. The Open-Response Questions section asks students about their experience, and Limitations suggests annotating where ChatGPT assisted. These support examples of documented and critical AI use, but the paper does not describe a general set of reflection prompts or AI-use reports aimed at evaluating both how and why students used AI. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPerceived trustworthiness of ChatGPT predicted students' intent to rely upon it for future educational purposes.
“For example, Tossell et al. (2024) found perceived trustworthiness of ChatGPT predicted students' intent to rely upon it for future educational purposes.”
From College students’ literacy, ChatGPT activities, educational outcomes, and trust from a digital divide perspective (2026) · cites this paper as Tossell et al., 2024
Students use ChatGPT to complete their homework assignments.
“students are using ChatGPT to complete their homework assignments [3]”
From Survey on Plagiarism Detection in Large Language Models: The Impact of ChatGPT and Gemini on Academic Integrity (2024) · cites this paper as [3]
University students value ChatGPT for its utility in completing academic tasks, such as writing assistance, research, and learning new concepts.
“This finding suggests that university students, in particular, value ChatGPT for its utility in completing academic tasks, such as writing assistance, research, and learning new concepts [1,7,20,65].”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [20]
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Cites
An integrative model of organizational trust
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995) · The Academy of Management Review · doi:10.2307/258792 · Cited by 10,736 · see citing works
Cohen, J. (1992) · Psychological Bulletin · doi:10.1037//0033-2909.112.1.155 · Cited by 8,771 · see citing works
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 2× · see citing works
“…responsibility for the essay produced with its help, this report provides the first comprehensive trust assessment for ChatGPT, which has strong validity due to the real vulnerability and consequences associated with this assignment; a requirement for accurate trust assessment [43], [48], [80].”
BackgroundMethodDwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584 · see citing works
Humans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554 · see citing works
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 2× · see citing works
“…resultsuggeststhatstudentsunderstoodChatGPTs’capabilities butalsorealizedthattheycouldnotfullydependonitalone tocompletetheassignment;aconclusionconsistentwiththe decadesofhumanfactorsautomationresearchthatdemonstrates automationdoesnotreplacethehumanbutchangesthewaywe workwithtechnology[15],[77].”
MethodBaidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004 · see citing works
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921 · see citing works
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817 · see citing works
The effect of the performance appraisal system on trust for management: A field quasi-experiment.
Mayer, R. C., & Davis, J. H. (1999) · Journal of Applied Psychology · doi:10.1037/0021-9010.84.1.123 · Cited by 2,248 · see citing works
Experimental evidence on the productivity effects of generative artificial intelligence
Noy, S., & Zhang, W. (2023) · Science · doi:10.1126/science.adh2586 · Cited by 2,150 · cited 1× · see citing works
“It is possible that the beneficial or detrimental effects of ChatGPT are more subtle, such as the finding that the tool is particularly beneficial for weaker performers [87].”
BackgroundBAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 1,826 · see citing works
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · cited 1× · see citing works
“In addition, AIs limited knowledge base and inconsistent factual precision have been recognized as significant drawbacks [25].”
BackgroundBayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
Wagenmakers, E., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., et al. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1343-3 · Cited by 1,652 · see citing works
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526 · see citing works
The role of trust in automation reliance
Dzindolet, M. T., Peterson, S. A., Pomranky, R. A., Pierce, L. G., & Beck, H. P. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00038-7 · Cited by 1,276 · see citing works
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080 · cited 2× · see citing works
“Beyond support in the traditional educational settings, there have been several applications of AI-based tools to enhance personalized learning, better support students with disabilities and inclusivity, and help to make teaching and grading more efficient [10].”
BackgroundModeling and remodeling writing
Hayes, J. R. (2012) · Written Communication · doi:10.1177/0741088312451260 · Cited by 893 · see citing works
ChatGPT in higher education: considerations for academic integrity and student learning
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.17 · Cited by 853 · cited 4× · see citing works
“In education, the launch of ChatGPT has resulted in mixed feelings among educators [3], popular media [4], and researchers [5] with some declaring the college essay now dead [6] or banning the use of ChatGPT [7].”
BackgroundMethodInfluentialEngineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education
Qadir, J. (2023) · 2023 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon54358.2023.10125121 · Cited by 707 · cited 1× · see citing works
“In Science, Technology, Engineering, Mathematics (STEM) education, ChatGPT has shown promise in physics [20], math [21], design [14], and engineering [22].”
BackgroundCollaborating with ChatGPT in argumentative writing classrooms
Su, Y., Lin, Y., & Lai, C. (2023) · Assessing Writing · doi:10.1016/j.asw.2023.100752 · Cited by 679 · see citing works
Harnessing the power of llms in practice: a survey on chatGPT and beyond
Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Zhong, S., Yin, B., & Hu, X. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3649506 · Cited by 644 · see citing works
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Yin, B., & Hu, X. (2023) · arXiv · doi:10.48550/arxiv.2304.13712 · Cited by 644 · see citing works
Mathematical Capabilities of ChatGPT
Frieder, S., Pinchetti, L., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P., Chevalier, A., & Berner, J. (2023) · Neural Information Processing Systems · doi:10.52202/075280-1205 · Cited by 630 · see citing works
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N. J., Barron, A. H., & Schelble, B. G. (2020) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 623 · see citing works
Merritt, S. M., & Ilgen, D. R. (2008) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872008x288574 · Cited by 527 · cited 1× · see citing works
“Finally, we measured propensity to trust, a construct used to capture individual difference attitudes toward trust in machines [48], [49], [50], [51], [52].”
MethodHuman Performance Consequences of Stages and Levels of Automation: An Integrated Meta-Analysis
Onnasch, L., Wickens, C. D., Li, H., & Manzey, D. (2014) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720813501549 · Cited by 524 · see citing works
Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey
Shoufan, A. (2023) · IEEE Access · doi:10.1109/access.2023.3268224 · Cited by 499 · cited 5× · see citing works
“Engineering students found it helpful in a design project for a college engineering course [14] and believe that large language models (LLMs) that power applications, such as ChatGPT, can change education for the better [9].”
BackgroundInfluentialInvestigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis
Choudhury, A., & Shamszare, H. (2023) · Journal of Medical Internet Research · doi:10.2196/47184 · Cited by 470 · see citing works
Gogol, K., Brunner, M., Goetz, T., Martin, R., Ugen, S., Keller, U., Fischbach, A., & Preckel, F. (2014) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2014.04.002 · Cited by 451 · see citing works
What ChatGPT means for universities: Perceptions of scholars and students
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.22 · Cited by 431 · cited 2× · see citing works
“These technologies also have the potential to make completing mundane tasks more efficient [9], including grading, answering common student queries, virtual tutoring, and developing course plans and materials [10].”
BackgroundHumans: Still Vital After All These Years of Automation
Parasuraman, R., & Wickens, C. D. (2008) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872008x312198 · Cited by 401 · see citing works
Trust in Artificial Intelligence: Meta-Analytic Findings
Kaplan, A. D., Kessler, T. T., Brill, J. C., & Hancock, P. A. (2023) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211013988 · Cited by 397 · see citing works
Mathematical Capabilities of ChatGPT
Frieder, S., Pinchetti, L., Chevalier, A., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P. C., & Berner, J. (2023) · arXiv · doi:10.48550/arxiv.2301.13867 · Cited by 385 · see citing works
I Trust It, but I Don’t Know Why
Merritt, S. M., Heimbaugh, H., LaChapell, J., & Lee, D. (2013) · Hum. Factors · doi:10.1177/0018720812465081 · Cited by 354 · see citing works
An Introduction to Bayesian Hypothesis Testing for Management Research
Andraszewicz, S., Scheibehenne, B., Rieskamp, J., Grasman, R. P. P. P., Verhagen, J., & Wagenmakers, E. (2014) · Journal of Management · doi:10.1177/0149206314560412 · Cited by 344 · see citing works
Situation awareness-based agent transparency and human-autonomy teaming effectiveness
Chen, J. Y. C., Lakhmani, S. G., Stowers, K., Selkowitz, A. R., Wright, J. L., & Barnes, M. (2018) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2017.1315750 · Cited by 341 · see citing works
Assessment in Engineering Education: Evolution, Approaches and Future Collaborations
Olds, B. M., Moskal, B. M., & Miller, R. L. (2005) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2005.tb00826.x · Cited by 338 · see citing works
Measurement of Trust in Automation: A Narrative Review and Reference Guide
Kohn, S. C., de Visser, E. J., Wiese, E., Lee, Y., & Shaw, T. H. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.604977 · Cited by 337 · see citing works
Roth, A., Ogrin, S., & Schmitz, B. (2016) · Educational Assessment, Evaluation and Accountability · doi:10.1007/s11092-015-9229-2 · Cited by 294 · see citing works
How Chat GPT Can Transform Autodidactic Experiences and Open Education?
Firat, M. (2023) · doi:10.31219/osf.io/9ge8m · Cited by 280 · see citing works
A multidimensional conception and measure of human-robot trust
Malle, B. F., & Ullman, D. L. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819472-0.00001-0 · Cited by 264 · see citing works
Automated Writing Assessment in the Classroom
Warschauer, M., & Grimes, D. (2008) · Pedagogies: An International Journal · doi:10.1080/15544800701771580 · Cited by 243 · see citing works
Mackieson, P., Shlonsky, A., & Connolly, M. (2019) · Qualitative Social Work · doi:10.1177/1473325018786996 · Cited by 242 · see citing works
Utility in a Fallible Tool: A Multi-Site Case Study of Automated Writing Evaluation.
Grimes, D., & Warschauer, M. (2010) · The Journal of Technology, Learning and Assessment (Boston College) · Cited by 241 · see citing works
Automation- Induced "Complacency": Development of the Complacency-Potential Rating Scale
Singh, I. L., Molloy, R., & Parasuraman, R. (1993) · International Journal of Aviation Psychology · doi:10.1207/s15327108ijap0302_2 · Cited by 219 · see citing works
McGuirl, J. M., & Sarter, N. B. (2006) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872006779166334 · Cited by 212 · see citing works
Muenks, K., Canning, E. A., LaCosse, J., Green, D. J., Zirkel, S., Garcia, J. A., & Murphy, M. (2020) · Journal of experimental psychology. General · doi:10.1037/xge0000763 · Cited by 211 · see citing works
de Visser, E., & Parasuraman, R. (2011) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343411410160 · Cited by 207 · see citing works
Effects of differential feedback on students’ examination performance.
Lipnevich, A. A., & Smith, J. K. (2009) · Journal of Experimental Psychology: Applied · doi:10.1037/a0017841 · Cited by 199 · see citing works
The death of the short-form physics essay in the coming AI revolution
Yeadon, W., Inyang, O.-O., Mizouri, A., Peach, A., & Testrow, C. P. (2023) · Physics Education · doi:10.1088/1361-6552/acc5cf · Cited by 199 · see citing works
The Death of the Short-Form Physics Essay in the Coming AI Revolution
Yeadon, W., Inyang, O.-O., Mizouri, A., Peach, A., & Testrow, C. (2022) · arXiv · doi:10.48550/arxiv.2212.11661 · Cited by 199
Kelter, R. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-00980-6 · Cited by 173 · see citing works
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC EraPreprint
Zhang, C.-N., Zhang, C.-S., Li, C., Qiao, Y., Zheng, S., Dam, S. K., Zhang, M.-C., Kim, J. U., Kim, S., Choi, J., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2304.06488 · Cited by 158 · see citing works
KABER, D. B. (2017) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343417737203 · Cited by 154 · see citing works
You can lead a horse to water but you cannot make him learn: Smartphone use in higher education
Tossell, C. C., Kortum, P. T., Shepard, C. W., Rahmati, A., & Zhong, L. (2014) · British Journal of Educational Technology · doi:10.1111/bjet.12176 · Cited by 140 · see citing works
Self-citationTopsakal, O., & Topsakal, E. (2022) · The Journal of Cognitive Systems · doi:10.52876/jcs.1227392 · Cited by 125 · see citing works
Measuring Gains and Losses in Human-Robot Trust: Evidence for Differentiable Components of Trust
Ullman, D. L., & Malle, B. F. (2019) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2019.8673154 · Cited by 114 · see citing works
Automation-Induced Complacency Potential: Development and Validation of a New Scale
Merritt, S. M., Ako-Brew, A., Bryant, W. J., Staley, A., McKenna, M., Leone, A., & Shirase, L. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00225 · Cited by 113 · see citing works
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC EraPreprint
Zhang, C., Zhang, C., Li, C., Qiao, Y., Zheng, S., Dam, S. K., Zhang, M., Kim, J. U., Kim, S. T., Park, G., et al. (2023) · arXiv (Cornell University) · doi:10.13140/rg.2.2.24789.70883 · Cited by 94 · see citing works
Elbow, P. H. (2000) · doi:10.1093/oso/9780195104158.001.0001 · Cited by 93 · see citing works
Which do students prefer to evaluate their essays: peers or computer program
Lai, Y. (2010) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2009.00959.x · Cited by 92 · see citing works
Individual differences in human–machine trust: A multi-study look at the perfect automation schema
Lyons, J. B., & Guznov, S. (2018) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2018.1491071 · Cited by 91 · see citing works
Brummelen, J. V., Tabunshchyk, V., & Heng, T. (2021) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3459990.3460730 · Cited by 79 · see citing works
Trust and Distrust of Automated Parking in a Tesla Model X
Tenhundfeld, N. L., de Visser, E. J., Ries, A. J., Finomore, V. S., & Tossell, C. C. (2019) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720819865412 · Cited by 78 · see citing works
Self-citationManohar, N., & Prasad, S. S. (2023) · Cureus · doi:10.7759/cureus.34616 · Cited by 63 · see citing works
Believe in artificial intelligence? A user study on the chatGPT’s fake information impact.
Amaro, I., Barra, P., Greca, A. D., Francese, R., & Tucci, C. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3291539 · Cited by 60 · cited 1× · see citing works
“WhileChatGPTwasconsidered trustworthyoverall,intenttorelyonChatGPTwaslower,which isconsistentwithfindingsfrompreviouswork[42],[85].”
Integrating a Social Robot in Higher Education – A Field Study
Donnermann, M., Schäper, P., & Lugrin, B. (2020) · IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) · doi:10.1109/ro-man47096.2020.9223602 · Cited by 43 · see citing works
ChatGPT by OpenAI: Students' Viewpoint on Cheating using Artificial Intelligence-Based ApplicationPreprint
Ventayen, R. J. M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4361548 · Cited by 41 · see citing works
Benefits of Peer Review on Students’ Writing
Yalch, M. M., Vitale, E. M., & Ford, J. K. (2019) · Psychology Learning & Teaching · doi:10.1177/1475725719835070 · Cited by 40 · see citing works
Does Automated Feedback Improve Writing Quality
Wilson, J., Olinghouse, N. G., & Andrada, G. N. (2014) · Learning disabilities · Cited by 35 · see citing works
Haber‐Curran, P., & Tillapaugh, D. (2013) · Journal of Leadership Education · doi:10.12806/v12/i1/92 · Cited by 25 · see citing works
Momen, A., Visser, E. D., Wolsten, K., Cooley, K., Walliser, J., & Tossell, C. C. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.063 · Cited by 21 · see citing works
Self-citationSchmid, D., & Stanton, N. A. (2019) · Human-Intelligent Systems Integration · doi:10.1007/s42454-020-00012-0 · Cited by 19 · see citing works
Monfort, S. S., Graybeal, J. J., Harwood, A. E., McKnight, P. E., & Shaw, T. H. (2017) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2017.1397228 · Cited by 18 · see citing works
An Evaluation of Short-Term Distributed Online Learning Events.
Barker, B. S., & Brooks, D. W. (2005) · International journal on e-learning · Cited by 15 · see citing works
Haber‐Curran, P., & Tillapaugh, D. (2013) · Journal of Leadership Education · doi:10.12806/v12/i1/r6 · Cited by 11 · see citing works
Teaching, Learning, and Thinking About Teaching and Learning
McKeachie, W. J. (1999) · Higher education · doi:10.1007/978-94-011-3955-7_1 · Cited by 10 · see citing works
Brummelen, J. V., Tabunshchyk, V., & Heng, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01367 · Cited by 10 · see citing works
Tenhundfeld, N. L. (2023) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/21695067231192916 · Cited by 9 · see citing works
Self-citationValidity of Single-Item Self-Report Measure of Learning Engagement
Łukowicz, P., Choynowska, A., Świątkowska, A. M., Bereznowski, P., Wróbel, W. K., & Atroszko, P. A. (2017) · CeON Repository (Centre for Evaluation in Education and Science) · Cited by 7 · see citing works
Those Who Can Do: Implementing Active Learning
Miserandino, M. (1998) · APS observer · Cited by 6 · see citing works
An Approach to Evaluating Distance Learning Events
Wisher, R. A., & Curnow, C. K. (1998) · doi:10.21236/ada360984 · Cited by 2 · see citing works
Ebbs, Flows, and Progress in the Teaching of Psychology
McKeachie, W. J. (2013) · Psychology Press eBooks · doi:10.4324/9781410603586-50 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- C. Teddlie and A. Tashakkori, "Major issues and controversies in the use of mixed methods in the social and behavioural sciences," in Handbook of Mixed Methods in Social and Behavioural Research, Sage, 2003, pp. 3-50.
- A. Bartoszko, S. Czerwinski, and A. Bereznowska, (Jun. 2019). Single-item self-report measure of learning engagement: What does it measure? Presented at Interdisciplinary Scientific International Conference for PhD Students and Assistants. [Paper]. Available: https://www.researchgate.net/publication/334974047_Single- item_self- report_measure_of_learning_engagement_What_does_it_measu re
- T. V. McGovern, "Process/pedagogy," in The teaching of psychology: Essays in honors of Wilbert J. McKeachie and Charles L. Brewer, Mahwah, NJ, Lawrence Erlbaum Associates Inc, 2002, pp. 81-90.
Cited by
Sorted by citations
Kong, S. C., & Yang, Y. P. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3392830 · Cited by 259 · see citing works
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e31887 · Cited by 194 · see citing works
Al-Abdullatif, A. M. (2024) · Education Sciences · doi:10.3390/educsci14111209 · Cited by 169 · see citing works
An LLM-Driven Chatbot in Higher Education for Databases and Information Systems
Neumann, A. T., Yin, Y., Sowe, S. K., Decker, S., & Jarke, M. (2024) · IEEE Transactions on Education · doi:10.1109/te.2024.3467912 · Cited by 187 · see citing works
Almulla, M. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32220 · Cited by 161 · see citing works
Al-Abdullatif, A. M., & Alsubaie, M. A. (2024) · Behavioral Sciences · doi:10.3390/bs14090845 · Cited by 126 · see citing works
“Students who have more experience engaging with AI tools are better at navigating risks and understanding the limitations of these technologies.”
Chen, A., Xiang, M., Zhou, J., Jia, J., Shang, J., Li, X., Gašević, D., & Fan, Y. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105198 · Cited by 115 · see citing works
Qadhi, S. M., Alduais, A., Chaaban, Y., & Khraisheh, M. (2024) · Information · doi:10.3390/info15060325 · Cited by 106 · see citing works
Policy guidelines and recommendations on AI use in teaching and learning: A meta-synthesis study
Funa, A. A., & Gabay, R. A. E. (2024) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2024.101221 · Cited by 145 · see citing works
Nazaretsky, T., Mejia‐Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100368 · Cited by 146 · see citing works
Li, B., Bonk, C. J., Wang, C., & Kou, X. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3386098 · Cited by 100 · see citing works
Zhang, X., Zhang, P., Shen, Y., Liu, M., Wang, Q., Gašević, D., & Fan, Y. (2024) · Frontiers of Digital Education · doi:10.1007/s44366-024-0028-5 · Cited by 83 · see citing works
Zhang, C., Hu, M., Wu, W., Kamran, F., & Wang, X. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12905-7 · Cited by 95 · see citing works
Automated Essay Scoring and Revising Based on Open-Source Large Language Models
Song, Y., Zhu, Q., Wang, H., & Zheng, Q. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3396873 · Cited by 82 · see citing works
Pudasaini, S., Miralles-Pechuán, L., Lillis, D., & Llorens Salvador, M. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-024-09576-x · Cited by 70 · see citing works
Filippi, S., & Motyl, B. (2024) · Information · doi:10.3390/info15060345 · Cited by 80 · see citing works
Beyond Traditional Assessment: Exploring the Impact of Large Language Models on Grading Practices
Fagbohun, O., Iduwe, N. P., Abdullahi, M., Ifaturoti, A., & Nwanna, O. (2024) · Journal of Artificial Intelligence, Machine Learning and Data Science · doi:10.51219/jaimld/oluwole-fagbohun/19 · Cited by 68 · see citing works
Ma, W., Wen-jing, M., Hu, Y., & Xinyu, B. I. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13128-6 · Cited by 71 · see citing works
Analyzing student prompts and their effect on ChatGPT’s performance
Sawalha, G., Taj, I., & Shoufan, A. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2397200 · Cited by 74 · see citing works
Chen, A., Jia, J., Li, Y., & Fu, L. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241299058 · Cited by 53 · see citing works
ChatPRCS: A Personalized Support System for English Reading Comprehension Based on ChatGPT
Wang, X., Zhong, Y., Huang, C., & Huang, X. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3405747 · Cited by 57 · see citing works
Exploring the influence of ChatGPT on student academic success and career readiness
Dahri, N. A., Yahaya, N., & Al-Rahmi, W. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13148-2 · Cited by 63 · see citing works
EduPlanner: LLM-Based Multiagent Systems for Customized and Intelligent Instructional Design
Zhang, X., Zhang, C., Sun, J., Xiao, J., Yang, Y., & Luo, Y. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3561332 · Cited by 46 · see citing works
Arar, K., Tlili, A., Schunka, L., Salha, S., & Saiti, A. (2025) · Leadership and Policy in Schools · doi:10.1080/15700763.2025.2451982 · Cited by 43 · see citing works
Grading the Graders: Comparing Generative AI and Human Assessment in Essay Evaluation
Wetzler, E. L., Cassidy, K. S., Jones, M. J., Frazier, C. R., Korbut, N., Sims, C. M., Bowen, S. S., & Wood, M. (2024) · Teaching of Psychology · doi:10.1177/00986283241282696 · Cited by 47 · see citing works
Zhang, C. (., Rice, R. E., & Wang, L. H. (2026) · New Media & Society · doi:10.1177/14614448241301741 · Cited by 47 · see citing works
“Overall, our findings extend past research on trust and technology adoption to the novel context of ChatGPT use and build on previous ChatGPT research (Choudhury and Shamszare, 2023; Tossell et al., 2024) by testing the moderating role of trust.”
Generative AI and College Students: Use and Perceptions
Golding, J. M., Lippert, A., Neuschatz, J. S., Salomon, I., & Burke, K. C. (2024) · Teaching of Psychology · doi:10.1177/00986283241280350 · Cited by 40 · see citing works
Uppal, K., & Hajian, S. (2024) · European Journal of Educational Research · doi:10.12973/eu-jer.14.1.199 · Cited by 36 · see citing works
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024) · IEEE Access · doi:10.1109/access.2024.3498047 · Cited by 44 · see citing works
Impact of GPT-Driven Teaching Assistants in VR Learning Environments
Tracy, K., & Spantidi, O. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3539179 · Cited by 30 · see citing works
Li, Z., & Xiao, Y. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07669-8 · Cited by 33 · see citing works
Wu, H., & Dong, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3544489 · Cited by 31 · see citing works
Cui, Y. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100391 · Cited by 26 · see citing works
Friction: Deciphering Writing Feedback into Writing Revisions through LLM-Assisted Reflection
Zhang, C., Ju, K., Bidoshi, P., Yen, Y., & Rzeszotarski, J. M. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714316 · Cited by 46 · see citing works
Durgungöz, A., & Kharrufa, A. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2509276 · Cited by 32 · see citing works
Nazaretsky, T., Mejia‐Domenzain, P., Swamy, V., Frej, J., & Käser, T. (2026) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70153 · Cited by 26 · see citing works
Empowering Student Learning Through Artificial Intelligence: A Bibliometric Analysis
Yun, G., Lee, K. M., & Choi, H. H. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241278636 · Cited by 26 · see citing works
Jayasinghe, S., Gamage, K. A. A., Yang, D., Cheng, C., Disanayake, C., & Apeji, U. D. (2026) · Education Sciences · doi:10.3390/educsci16010137 · Cited by 23 · see citing works
Xiao-hua, L., Xiao, Y., & Li, D. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00567-5 · Cited by 31 · see citing works
Alghazo, R., Fatima, G., Malik, M., Abdelhamid, S. E., Jahanzaib, M., Nayab, D. E., & Raza, A. (2025) · Education Sciences · doi:10.3390/educsci15020158 · Cited by 22 · see citing works
Zhu, Y., Jiang, H., & Zhong, L. (2026) · European Journal of Education · doi:10.1111/ejed.70462 · Cited by 23 · see citing works
Ali, M., Rehman, S., & Cheema, E. U. (2025) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-025-01876-5 · Cited by 20 · see citing works
Generative artificial intelligence in fifth-generation education systems: A systematic review
Chakraborty, S. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114463 · Cited by 50 · see citing works
Yang, C. T.-Y., & Chen, H. H.-J. (2025) · Computer Assisted Language Learning · doi:10.1080/09588221.2025.2453205 · Cited by 18 · see citing works
Suchánek, P., & Kŕaĺová, M. (2025) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2025.100781 · Cited by 17 · see citing works
Examining Student and Teacher Perspectives on Undisclosed Use of Generative AI in Academic Work
Adnin, R., Pandkar, A., Yao, B., Wang, D., & Das, M. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713393 · Cited by 16 · see citing works
Guo, Y., Shen, S., Liu, Q., Huang, Z., Zhu, L., Su, Y., & Chen, E. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679664 · Cited by 16 · see citing works
Bai, L., & Wei, Y. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3491864 · Cited by 21 · see citing works
Mirea, C.-M., Bologa, R., Toma, A., Clim, A., Plăcintă, D.-D., & Bobocea, A. (2025) · Applied Sciences · doi:10.3390/app15105785 · Cited by 27 · see citing works
Rücker, C. R., & Becker, S. (2025) · Computers and Education Open · doi:10.1016/j.caeo.2025.100315 · Cited by 15 · see citing works
Santhi, T. M., & Srinivasan, K. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3417252 · Cited by 16 · see citing works
Choi, K. Y., Wu, C., & Moorhouse, B. L. (2025) · Technology in Language Teaching & Learning · doi:10.29140/tltl.v7n3.102516 · Cited by 13 · see citing works
Gawlik-Kobylińska, M. (2024) · Education Sciences · doi:10.3390/educsci14101132 · Cited by 14 · see citing works
Divisional perspectives on trust in generative AI: an empirical study of senior decision-makers
Khac, L. T. D., Leyer, M., Wichmann, J., & Richter, A. (2025) · Enterprise Information Systems · doi:10.1080/17517575.2025.2481557 · Cited by 13 · see citing works
Yu, Y., Wei, W., & Chen, Z. (2025) · The Interpreter and Translator Trainer · doi:10.1080/1750399x.2025.2533069 · Cited by 15 · see citing works
Understanding user trust in AI-generated content: an elaboration likelihood model perspective
Zhou, T., & Fang, X. (2025) · Online Information Review · doi:10.1108/oir-01-2025-0041 · Cited by 16 · see citing works
Evaluating the Potential of Generative Artificial Intelligence to Innovate Feedback Processes
Huesca, G., Elizondo-García, M. E., Aguayo-González, R., Aguayo-Hernández, C. H., González-Buenrostro, T., & Verdugo-Jasso, Y. A. (2025) · Education Sciences · doi:10.3390/educsci15040505 · Cited by 13 · see citing works
Diyab, A., Frost, R. M., Fedoruk, B. D., & Diyab, A. (2025) · Education Sciences · doi:10.3390/educsci15020156 · Cited by 13 · see citing works
Zhang, Y., Guo, J., Wang, Y., Li, S., Yang, Q., Zhang, J., & Lu, Z. (2025) · Systems · doi:10.3390/systems13100855 · Cited by 16 · see citing works
Pirzado, F. A., Ahmed, A., Alipour, M. A., & Terashima‐Marín, H. (2025) · Journal of Computers in Education · doi:10.1007/s40692-025-00375-w · Cited by 16 · see citing works
Hanelt, A., Wiesche, M., Benlian, A., & Hinz, O. F. (2025) · MIS Quarterly · doi:10.25300/misq/2025/18041 · Cited by 12 · see citing works
The Importance of Collaboration Between Human Intelligence and GenAI in Digitalization of Education
Yakin, A. M. A., Rasyid, A. R., Massyat, M., Muthmainnah, M., Aquino, A. B., Noche, T. H., & Cardoso, L. (2024) · Advances in computer and electrical engineering book series · doi:10.4018/979-8-3693-5762-0.ch006 · Cited by 11 · see citing works
Zou, D., Gu, M. M., & Yu, G. (2025) · System · doi:10.1016/j.system.2025.103783 · Cited by 13 · see citing works
What Shapes Learners’ Trust in AI? A Meta-Analytic Review of Its Antecedents and Consequences
Cui, Y., Zeng, M. L., Du, X. K., & He, W. M. (2025) · IEEE Access · doi:10.1109/access.2025.3611367 · Cited by 17 · see citing works
Chatbot in education: trends, personalisation, and techniques
Jusoh, S., & Kadir, R. A. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20659-8 · Cited by 11 · see citing works
Kohn, T. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70043 · Cited by 13 · see citing works
Gong, L., Chen, J., & Wu, F. Q. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3564177 · Cited by 10 · see citing works
Martha, A. S. D., Widowati, S., & Rahayu, D. P. (2025) · Journal of Information Technology Education Research · doi:10.28945/5599 · Cited by 18 · see citing works
Pudasaini, S., Miralles-Pechuán, L., Lillis, D., & Salvador, M. L. (2024) · arXiv · doi:10.48550/arxiv.2407.13105 · Cited by 8 · see citing works
“For example, students are using ChatGPT to complete their homework assignments [3] and to pass challenging exams like the Graduate Record Examination (GRE) and Scholastic Assessment Test (SAT) [4].”
MethodThey Prefer Humans! Experimental Measurement of Student Trust in ChatGPT
Walker, F., Favetta, M., Hasker, L., & Walker, R. D. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650955 · Cited by 11 · see citing works
A Diffusion-Based Cognitive Diagnosis Framework for Robust Learner Assessment
Zhao, G., Huang, Z., Zhuang, Y., Bi, H., Wang, Y., Wang, F., Ma, Z., & Zhao, Y. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3492214 · Cited by 9 · see citing works
Liu, S., Liang, C., & Yu, S. (2025) · System · doi:10.1016/j.system.2025.103831 · Cited by 10 · see citing works
Ding, J., & Song, Y. (2025) · Educational Psychology · doi:10.1080/01443410.2025.2568189 · Cited by 13 · see citing works
Bashraheel, M., & Ghinea, G. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.100929 · Cited by 14 · see citing works
Sozon, M., Pok, W. F., Sia, B. C., & Alkharabsheh, O. H. M. (2024) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-12-2023-0558 · Cited by 8 · see citing works
The Algorithm of Fear: Unpacking Prejudice Against AI and the Mistrust of Technology
Hutson, J., & Plate, D. (2024) · Journal of Innovation and Technology · doi:10.61453/joit.v2024no38 · Cited by 8 · see citing works
Almogren, A. S. (2025) · IEEE Access · doi:10.1109/access.2025.3553631 · Cited by 8 · see citing works
Trust(worthiness) Issues with Trust in Human–Robot Interaction
Onnasch, L., Roesler, E., Robert, L., & de Visser, E. J. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3778865 · Cited by 9 · see citing works
Guardia-Paniura, C. H., Cueva-Luza, T., Cruz-Carpio, F. M., Ito-Díaz, R. R., Apaza-Paco, D. V., Rosas-Rojas, N., Mamani-Mamani, B., Terrero-Pérez, Á., Yaedú, R. Y. F., & Peralta-Mamani, M. (2026) · Contemporary Educational Technology · doi:10.30935/cedtech/17863 · Cited by 8 · see citing works
Li, X., Li, T., Wang, M., Tao, S., Zhou, X., Wei, X., & Guan, N. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3539104 · Cited by 8 · see citing works
Huang, M., Cheong, N., Zhang, Z., & Liu, J. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3610636 · Cited by 7 · see citing works
ChatGPT and academic work: new psychological phenomena
de Winter, J., Hancock, P. A., & Eisma, Y. B. (2025) · AI & Society · doi:10.1007/s00146-025-02241-w · Cited by 7 · see citing works
Dindorf, C., Weisenburger, F., Bartaguiz, E., Dully, J., Klappenberger, L., Lang, V., Zimmermann, L., Fröhlich, M., & Seibert, J. (2024) · Education Sciences · doi:10.3390/educsci14050531 · Cited by 6 · see citing works
Brocato, E. D., & Davis, C. D. (2025) · Journal of Marketing Education · doi:10.1177/02734753241312350 · Cited by 8 · see citing works
Detecting AI-Generated Writing Using GPTZero
Paullet, K., Pinchot, J., Kinney, E., & Stewart, T. (2025) · Information Systems Education Journal · doi:10.62273/pzww7741 · Cited by 5 · see citing works
The Effect of Feedback on Informative Text Writing: AI or Teacher?
Baz, M. A., & Hasırcı, S. (2025) · Open Praxis · doi:10.55982/openpraxis.17.3.871 · Cited by 12 · see citing works
Demir, M., Miratsky, J. A., Mishra, P., Nguyen, J. T., Chan, C. K., & Singharoy, A. (2025) · Cognitive Computation · doi:10.1007/s12559-025-10498-y · Cited by 5 · see citing works
Alkabaa, A. S., & Alamri, N. M. H. (2025) · Computer Applications in Engineering Education · doi:10.1002/cae.70110 · Cited by 6 · see citing works
Perceived trustworthiness and moral competence of a GenAI-enabled ethical robot advisor
Momen, A., Tossell, C. C., Niemeyer, R. E., Walliser, J. C., Tolston, M. T., Funke, G. J., & de Visser, E. J. (2025) · Interaction Studies Social Behaviour and Communication in Biological and Artificial Systems · doi:10.1075/is.25072.mom · Cited by 5 · see citing works
Self-citationJapanese University EFL Student Insights on the Emerging ChatGPT Phenomenon
Price, G. (2024) · International Journal of Education Learning and Development · doi:10.37745/ijeld.2013/vol12n583100 · Cited by 5 · see citing works
Abdelwahed, N. A. A. (2024) · OBM Neurobiology · doi:10.21926/obm.neurobiol.2403241 · Cited by 5 · see citing works
Al-Shamaileh, O., Mubin, O., & Nizamuddin, N. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2532712 · Cited by 7 · see citing works
Zhang, C., Zhu, S., Yang, X., Tseng, Y.-C., Jiang, S., & Rzeszotarski, J. M. (2025) · Proceedings of the 7th ACM Conference on Conversational User Interfaces · doi:10.1145/3719160.3736618 · Cited by 4 · see citing works
AI chatbots in higher education: Comparing expectations to evidence
Thoeni, A., & Fryer, L. K. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101061 · Cited by 4 · see citing works
Kumar, V. (2025) · International Journal of Mechanical Engineering Education · doi:10.1177/03064190251374463 · Cited by 6 · see citing works
Pedraja-Rejas, L., Vega, P. L., & Huanca, P. R. (2025) · Behavioral Sciences · doi:10.3390/bs16010011 · Cited by 4 · see citing works
Qadhi, S. M., Alduais, A. M. S., Chaaban, Y., & Khraisheh, M. A. M. (2026) · The International Information & Library Review · doi:10.1080/10572317.2026.2626122 · Cited by 4 · see citing works
Elia, J. (2024) · Studies in big data · doi:10.1007/978-3-031-71213-5_11 · Cited by 4 · see citing works
Helm, G., & Hesse, F. (2025) · Journal of Writing Research · doi:10.17239/jowr-2025.17.01.01 · Cited by 5 · see citing works
Korchak, A., Patarakin, E., & Costley, J. (2025) · RUDN Journal of Informatization in Education · doi:10.22363/2312-8631-2025-22-1-37-57 · Cited by 5 · see citing works
- Authors
- Tossell, C. C., Tenhundfeld, N. L., Momen, A., Cooley, K., & de Visser, E. J.
- Affiliations
- United States Air Force Academy (US)
- University of Alabama in Huntsville (US)
- Department of Behavioral Sciences and Leadership, United States Air Force Academy, Colorado Springs, CO, USA
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2024
- Volume
- 17
- Pages
- 1069-1081
- ISSN
- 1939-1382 (ISSN-L)
- 2372-0050
- OpenAlex
- W4390921168
- Cited by
- 218
- References
- 79
- Citations by year
- 2026: 80 · 2025: 90 · 2024: 48
- FWCI
- 81.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Learning Technologies (publishedVersion, cc-by)
- Funders
- Air Force Office of Scientific Research (21USCOR004)
- Defense Advanced Research Projects Agency (FA8650-23-C-7318)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- ChatGPT, student perceptions, AI-assisted grading, trust in AI, academic integrity, human oversight of AI, engineering education, student learning experience
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