Promises and challenges of generative artificial intelligence for human learning
Gašević, D., Siemens, G. & Sadiq, S. Empowering learners for the age of artificial intelligence. Comput. Educ. Artif. Intell. 4, 100130 (2023).
Yan, L. et al. Practical and ethical challenges of large language models in education: a systematic scoping review. Br. J. Educ. Technol. 35, 90–112 (2023).
Dai, W. et al. Can large language models provide feedback to students? A case study on ChatGPT. In Proc. 2023 IEEE International Conference on Advanced Learning Technologies 323–325 (IEEE, 2023).
Li, Y. et al. Can large language models write reflectively. Comput. Educ. Artif. Intell. 4, 100140 (2023).
Yildirim-Erbasli, S. N. & Bulut, O. Conversation-based assessment: a novel approach to boosting test-taking effort in digital formative assessment. Comput. Educ. Artif. Intell. 4, 100135 (2023).
Mazzoli, C. A., Semeraro, F. & Gamberini, L. Enhancing cardiac arrest education: exploring the potential use of Midjourney. Resuscitation 189, 109893 (2023).
Vartiainen, H. & Tedre, M. Using artificial intelligence in craft education: crafting with text-to-image generative models. Digit. Creat. 34, 1–21 (2023).
Kasneci, E. et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Diff. 103, 102274 (2023).
Falcão, T. P., Mello, R. F. & Rodrigues, R. L. Applications of learning analytics in Latin America. J. Learn. Anal. 51, 871–874 (2020).
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D. & Siemens, G. Impact of AI assistance on student agency. Comput. Educ. 210, 104967 (2024).
Mousavinasab, E. et al. Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interact. Learn. Environ. 29, 142–163 (2021).
Vygotsky, L. S. & Cole, M. Mind in Society: Development of Higher Psychological Processes (Harvard Univ. Press, 1978).
Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M. & Siemens, G. Opportunities of artificial intelligence for supporting complex problem-solving: findings from a scoping review. Comput. Educ. Artif. Intell. 4, 100138 (2023).
Chang, Y. et al. A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol. 15, 1–45 (2024).
Meet Khanmigo: Khan Academy’s AI-powered teaching assistant & tutor. Khan Academy (2023).
Lee, V. S. What is inquiry-guided learning? New Dir. Teach. Learn. 129, 5–14 (2012).
Chan, C. K. Y. & Hu, W. Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. Int. J. Educ. Technol. High. Educ. 20, 43 (2023).
Hennessy, S., Cukurova, M., Lewin, C., Mavrikis, M. & Major, L. BJET Editorial 2024: a call for research rigour. Br. J. Educ. Technol. 55, 5–9 (2024).
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D. & Siemens, G. Impact of AI assistance on student agency. Comput. Educ. 210, 104967 (2024).
Nie, A. et al. The GPT surprise: offering large language model chat in a massive coding class reduced engagement but increased adopters exam performances. Preprint at arXiv (2024).
Molenaar, I. Towards hybrid human–AI learning technologies. Eur. J. Educ. 57, 632–645 (2022).
Ji, H., Han, I. & Ko, Y. A systematic review of conversational AI in language education: focusing on the collaboration with human teachers. J. Res. Technol. Educ. 55, 48–63 (2023).
Yang, K. B. et al. Surveying teachers’ preferences and boundaries regarding human–AI control in dynamic pairing of students for collaborative learning. In Proc. 16th European Conference on Technology Enhanced Learning 260–274 (Springer, 2021).
Pesovski, I., Santos, R., Henriques, R. & Trajkovik, V. Generative AI for customizable learning experiences. Sustainability 16, 3034 (2024).
Hwang, K., Wang, K., Alomair, M., Choa, F.-S. & Chen, L. K. Towards automated multiple choice question generation and evaluation: aligning with Bloom’s taxonomy. In Proc. 25th International Conference on Artificial Intelligence in Education 389–396 (Springer, 2024).
Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning 8748–8763 (PMLR, 2021).
Chiu, T. K. The impact of generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interact. Learn. Environ. (2023).
Google Scholar
Lee, U. et al. Prompt Aloud!: incorporating image-generative AI into STEAM class with learning analytics using prompt data. Educ. Inform. Technol. 29, 9575–9605 (2024).
Chen, Y., Zhang, X. & Hu, L. A progressive prompt-based image-generative AI approach to promoting students’ achievement and perceptions in learning ancient Chinese poetry. Educ. Technol. Soc. 27, 284–305 (2024).
Long, L., MacBlain, S. & MacBlain, M. Supporting students with dyslexia at the secondary level: an emotional model of literacy. J. Adolesc. Adult Lit. 51, 124–134 (2007).
Leiker, D., Gyllen, A. R., Eldesouky, I. & Cukurova, M. Generative AI for learning: investigating the potential of learning videos with synthetic virtual instructors. In Proc. 24th International Conference on Artificial Intelligence in Education 523–529 (Springer, 2023).
Bada, S. O. & Olusegun, S. Constructivism learning theory: a paradigm for teaching and learning. J. Res. Method Educ. 5, 66–70 (2015).
Tavakoli, M., Faraji, A., Molavi, M., Mol, S. T. & Kismihók, G. Hybrid human–AI curriculum development for personalised informal learning environments. In Proc. 12th International Learning Analytics and Knowledge Conference 563–569 (ACM, 2022).
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D. & Mirriahi, N. Using learning analytics to scale the provision of personalised feedback. Br. J. Educ. Technol. 50, 128–138 (2019).
Lim, L.-A. et al. What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learn. Instr. 72, 101202 (2021).
Hattie, J. & Timperley, H. The power of feedback. Rev. Educ. Res. 77, 81–112 (2007).
Poulos, A. & Mahony, M. J. Effectiveness of feedback: the students’ perspective. Assess. Eval. High. Educ. 33, 143–154 (2008).
Steiss, J. et al. Comparing the quality of human and ChatGPT feedback of students’ writing. Learn. Instr. 91, 101894 (2024).
Meyer, J. et al. Using llms to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions. Comput. Educ. Artif. Intell. 6, 100199 (2024).
Zhang, Z. et al. Students’ perceptions and preferences of generative artificial intelligence feedback for programming. In Proc. 38th AAAI Conference on Artificial Intelligence 23250–23258 (AAAI, 2024).
Liang, Z., Sha, L., Tsai, Y.-S., Gašević, D. & Chen, G. Towards the automated generation of readily applicable personalised feedback in education. In Proc. 25th International Conference on Artificial Intelligence in Education 75–88 (Springer, 2024).
Wiboolyasarin, W., Wiboolyasarin, K., Suwanwihok, K., Jinowat, N. & Muenjanchoey, R. Synergizing collaborative writing and AI feedback: an investigation into enhancing L2 writing proficiency in Wiki-based environments. Comput. Educ. Artif. Intell. 6, 100228 (2024).
Yan, L. et al. VizChat: enhancing learning analytics dashboards with contextualised explanations using multimodal generative AI chatbots. In Proc. 25th International Conference on Artificial Intelligence in Education 180–193 (Springer, 2024).
Matcha, W., Gašević, D. & Pardo, A. et al. A systematic review of empirical studies on learning analytics dashboards: a self-regulated learning perspective. IEEE Trans. Learn. Technol. 13, 226–245 (2019).
Yang, M. & Carless, D. The feedback triangle and the enhancement of dialogic feedback processes. Teach. High. Educ. 18, 285–297 (2013).
Dawson, P. et al. in Learning, Design, and Technology: An International Compendium of Theory, Research, Practice, and Policy 695–739 (Springer, 2023).
Wang, T. et al. RODIN: a generative model for sculpting 3D digital avatars using diffusion. In Proc. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition 4563–4573 (IEEE, 2023).
Le, M. et al. Voicebox: text-guided multilingual universal speech generation at scale. In Advances in Neural Information Processing Systems (eds Oh, A. et al.) 14005–14034 (Curran Associates, 2023).
McCarthy, J. Evaluating written, audio and video feedback in higher education summative assessment tasks. Issues Educ. Res. 25, 153–169 (2015).
Orlando, J. A comparison of text, voice, and screencasting feedback to online students. Am. J. Distance Educ. 30, 156–166 (2016).
Henderson, M. & Phillips, M. Video-based feedback on student assessment: scarily personal. Austral. J. Educ. Technol. 31, 51–66 (2015).
Swiecki, Z. et al. Assessment in the age of artificial intelligence. Comput. Educ. Artif. Intell. 3, 100075 (2022).
Wu, Q. et al. AutoGen: enabling next-gen LLM applications via multi-agent conversation. Preprint at arXiv (2023).
Park, J. S. et al. Generative agents: interactive simulacra of human behavior. In Proc. 36th Annual ACM Symposium on User Interface Software and Technology 1–22 (ACM, 2023).
Fan, Y. et al. Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacogn. Learn. 17, 949–987 (2022).
Allen, L. K., Creer, S. C. & Öncel, P. in The Handbook of Learning Analytics 2nd edn (eds Lang, C et al.) 46–53 (Society for Learning Analytics Research, 2022).
Gašević, D., Greiff, S. & Shaffer, D. W. Towards strengthening links between learning analytics and assessment: challenges and potentials of a promising new bond. Comput. Hum. Behav. 134, 107304 (2022).
Lagakis, P. & Demetriadis, S. EvaAI: a multi-agent framework leveraging large language models for enhanced automated grading. In Proc. 20th International Conference on Intelligent Tutoring Systems 378–385 (Springer, 2024).
Shahzad, R. et al. Multi-agent system for students cognitive assessment in e-learning environment. IEEE Access 12, 15458–15467 (2024).
Yang, K. et al. Content knowledge identification with multi-agent large language models (LLMs). In Proc. 25th International Conference on Artificial Intelligence in Education 284–292 (Springer, 2024).
Song, W. et al. An intelligent virtual standard patient for medical students training based on oral knowledge graph. IEEE Trans. Multimedia 25, 6132–6145 (2022).
Ji, S., Pan, S., Cambria, E., Marttinen, P. & Philip, S. Y. A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33, 494–514 (2021).
Rehm, J., Reshodko, I., Børresen, S. Z. & Gundersen, O. E. The virtual driving instructor: multi-agent system collaborating via knowledge graph for scalable driver education. In Proc. 38th AAAI Conference on Artificial Intelligence 22806–22814 (2024).
Jin, H., Lee, S., Shin, H. & Kim, J. Teach AI how to code: using large language models as teachable agents for programming education. In Proc. 2024 CHI Conference on Human Factors in Computing Systems 1–28 (ACM, 2024).
Yang, Q.-F., Lian, L.-W. & Zhao, J.-H. Developing a gamified artificial intelligence educational robot to promote learning effectiveness and behavior in laboratory safety courses for undergraduate students. Int. J. Educ. Technol. High. Educ. 20, 18 (2023).
Thanh, B. N. et al. Race with the machines: assessing the capability of generative AI in solving authentic assessments. Australas. J. Educ. Technol. 39, 59–81 (2023).
Chesler, N. C. et al. A novel paradigm for engineering education: virtual internships with individualized mentoring and assessment of engineering thinking. J. Biomech. Eng. 137, 024701 (2015).
Google Scholar
Cant, R. P. & Cooper, S. J. Simulation-based learning in nurse education: systematic review. J. Adv. Nurs. 66, 3–15 (2010).
Google Scholar
Maynez, J., Narayan, S., Bohnet, B. & McDonald, R. On faithfulness and factuality in abstractive summarization. In Proc. 58th Annual Meeting of the Association for Computational Linguistics 1906–1919 (Association for Computational Linguistics, 2020).
Ji, Z. et al. Survey of hallucination in natural language generation. ACM Comput. Surv. 55, 1–38 (2023).
Carlini, N. et al. Extracting training data from large language models. In Proc. 30th USENIX Security Symposium 2633–2650 (USENIX, 2021).
Borji, A. A categorical archive of ChatGPT failures. Preprint at arXiv (2023).
Chelli, M. et al. Hallucination rates and reference accuracy of ChatGPT and bard for systematic reviews: comparative analysis. J. Med. Internet Res. 26, e53164 (2024).
Google Scholar
Sahoo, N. R. et al. Addressing bias and hallucination in large language models. In Proc. 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation 73–79 (ELRA Language Resource Association, 2024).
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W. & Qiao, M. S. Conceptualizing AI literacy: an exploratory review. Comput. Educ. Artif. Intell. 2, 100041 (2021).
Leiser, F. et al. From ChatGPT to FactGPT: a participatory design study to mitigate the effects of large language model hallucinations on users. In Proc. Mensch Und Computer 2023 81–90 (Association for Computing Machinery, 2023).
Schneider, J., Richner, R. & Riser, M. Towards trustworthy autograding of short, multi-lingual, multi-type answers. Int. J. Artif. Intell. Educ. 33, 88–118 (2023).
Khosravi, H. et al. Explainable artificial intelligence in education. Comput. Educ. Artif. Intell. 3, 100074 (2022).
Yang, S. J., Ogata, H., Matsui, T. & Chen, N.-S. Human-centered artificial intelligence in education: seeing the invisible through the visible. Comput. Educ. Artif. Intell. 2, 100008 (2021).
Short, H. A critical evaluation of the contribution of trust to effective technology enhanced learning in the workplace: a literature review. Br. J. Educ. Technol. 45, 1014–1022 (2014).
Mutimukwe, C., Viberg, O., Oberg, L.-M. & Cerratto-Pargman, T. Students’ privacy concerns in learning analytics: model development. Br. J. Educ. Technol. 53, 932–951 (2022).
Brown, H., Lee, K., Mireshghallah, F., Shokri, R. & Tramèr, F. What does it mean for a language model to preserve privacy? In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 2280–2292 (ACM, 2022).
Nasr, M. et al. Scalable extraction of training data from (production) language models. Preprint at arXiv (2023).
Winograd, A. Loose-lipped large language models spill your secrets: the privacy implications of large language models. Harvard J. Law Technol. 36, 616–656 (2023).
Yao, Y. et al. A survey on large language model (LLM) security and privacy: the good, the bad, and the ugly. High Confid. Comput. 4, 100211 (2024).
Pugh, S. L. et al. Say what? Automatic modeling of collaborative problem solving skills from student speech in the wild. Proc. 14th International Conference on Educational Data Mining 55–67 (International Educational Data Mining Society, 2021).
Sha, L. et al. Assessing algorithmic fairness in automatic classifiers of educational forum posts. In Proc. 22nd International Conference on Artificial Intelligence in Education 381–394 (Springer, 2021).
Merine, R. & Purkayastha, S. Risks and benefits of AI-generated text summarization for expert level content in graduate health informatics. In Proc. 10th International Conference on Healthcare Informatics 567–574 (IEEE, 2022).
Sha, L., Raković, M., Das, A., Gašević, D. & Chen, G. Leveraging class balancing techniques to alleviate algorithmic bias for predictive tasks in education. IEEE Trans. Learn. Technol. 15, 481–492 (2022).
Sha, L., Li, Y., Gasevic, D. & Chen, G. Bigger data or fairer data? Augmenting BERT via active sampling for educational text classification. In Proc. 29th International Conference on Computational Linguistics 1275–1285 (International Committee on Computational Linguistics, 2022).
Wu, J. Analysis and evaluation of the impact of integrating mental health education into the teaching of university civics courses in the context of artificial intelligence. Wirel. Commun. Mob. Comput. (2022).
Google Scholar
Tlili, A. et al. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn. Environ. 10, 15 (2023).
EU AI act: first regulation on artificial intelligence. European Parliament (2023).
Mao, J., Chen, B. & Liu, J. C. Generative artificial intelligence in education and its implications for assessment. TechTrends 68, 58–66 (2023).
Yang, Z. et al. AppAgent: multimodal agents as smartphone users. Preprint at arXiv (2023).
Viberg, O., Hatakka, M., Bälter, O. & Mavroudi, A. The current landscape of learning analytics in higher education. Comput. Hum. Behav. 89, 98–110 (2018).
Siemens, G. et al. Human and artificial cognition. Comput. Educ. Artif. Intell. 3, 100107 (2022).
Järvelä, S. et al. Hybrid intelligence—human–AI co-evolution and learning in multirealities (HI). In Proc. 2nd International Conference on Hybrid Human–Artificial Intelligence 392–394 (IOS Press, 2023).
Long, D. & Magerko, B. What is AI literacy? Competencies and design considerations. In Proc. 2020 CHI Conference on Human Factors in Computing Systems 1–16 (ACM, 2020).
Weiser, B. Here’s what happens when your lawyer uses ChatGPT. The New York Times (28 May 2023).
Kabir, S., Udo-Imeh, D. N., Kou, B. & Zhang, T. Is stack overflow obsolete? an empirical study of the characteristics of chatgpt answers to stack overflow questions. In Proc. 2024 CHI Conference on Human Factors in Computing Systems 1–17 (ACM, 2024).
Bjork, R. A., Dunlosky, J. & Kornell, N. Self-regulated learning: beliefs, techniques, and illusions. Annu. Rev. Psychol. 64, 417–444 (2013).
Google Scholar
Kabir, S., Udo-Imeh, D. N., Kou, B. & Zhang, T. Is stack overflow obsolete? an empirical study of the characteristics of chatgpt answers to stack overflow questions. Preprint at arXiv (2023).
Rafner, J., Beaty, R. E., Kaufman, J. C., Lubart, T. & Sherson, J. Creativity in the age of generative AI. Nat. Hum. Behav. 7, 1836–1838 (2023).
Google Scholar
Shneiderman, B. Human-centered artificial intelligence: reliable, safe & trustworthy. Int. J. Hum. Comput. Interact. 36, 495–504 (2020).
Giannini, S. Generative artificial intelligence in education: think piece by Stefania Giannini. unesco.org (UNESCO, 2023).
Kung, T. H. et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLoS Digit. Health 2, e0000198 (2023).
Google Scholar
Choi, J. H., Hickman, K. E., Monahan, A. B. & Schwarcz, D. ChatGPT goes to law school. J. Leg. Educ. 71, 387 (2021).
Terwiesch, C. Would Chat GPT3 Get a Wharton MBA? A Prediction Based on its Performance in the Operations Management Course (Wharton University of Pennsylvania, 2023).
Zhang, S. J. et al. Exploring the MIT Mathematics and EECS curriculum using large language models. Preprint at arXiv (2023).
Chowdhuri, R., Deshmukh, N. & Koplow, D. No, GPT4 can’t ace MIT. Raunak Does Dev (2023).
Lorenz, P., Perset, K. & Berryhill, J. Initial Policy Considerations for Generative Artificial Intelligence (OECD, 2023).
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