Promises and challenges of generative artificial intelligence for human learning.

[en] Generative artificial intelligence (GenAI) holds the potential to transform the delivery, cultivation and evaluation of human learning. Here the authors examine the integration of GenAI as a tool for human learning, addressing its promises and challenges from a holistic viewpoint that integrates insights from learning sciences, educational technology and human-computer interaction. GenAI promises to enhance learning experiences by scaling personalized support, diversifying learning materials, enabling timely feedback and innovating assessment methods. However, it also presents critical issues such as model imperfections, ethical dilemmas and the disruption of traditional assessments. Thus, cultivating AI literacy and adaptive skills is imperative for facilitating informed engagement with GenAI technologies. Rigorous research across learning contexts is essential to evaluate GenAI's effect on human cognition, metacognition and creativity. Humanity must learn with and about GenAI, ensuring that it becomes a powerful ally in the pursuit of knowledge and innovation, rather than a crutch that undermines our intellectual abilities.

Disciplines :

Education & instruction

Author, co-author :

Yan, Lixiang ; Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia

GREIFF, Samuel ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences > Department of Behavioural and Cognitive Sciences > Team Samuel GREIFF ; Department of Educational Psychology, Goethe-University Frankfurt, Frankfurt, Germany. samuel.greiff@tum.de ; Centre for International Student Assessment (ZIB) & School of Social Sciences and Technology, Technical University of Munich, Munich, Germany. samuel.greiff@tum.de

TEUBER, Ziwen ; University of Luxembourg

Gašević, Dragan ; Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia. dragan.gasevic@monash.edu

External co-authors :

yes

Language :

English

Title :

Promises and challenges of generative artificial intelligence for human learning.

Publication date :

October 2024

Journal title :

Nature Human Behaviour

eISSN :

2397-3374

Publisher :

Nature Research, England

Volume :

Issue :

Pages :

1839 - 1850

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://www.nature.com/articles/s41562-024-02004-5.pdf

Funding text :

This study was supported by grants from the Australian Research Council (grant agreement numbers DP220101209 and DP240100069 to D.G.). L.Y.\u2019s work is fully funded by the Digital Health Cooperative Research Centre (DHCRC). D.G.\u2019s work was supported in part by the DHCRC and Defense Advanced Research Projects Agency (DARPA) through the Knowledge Management at Speed and Scale (KMASS) programme (HR0011-22-2-0047). The DHCRC is established and supported under the Australian Government\u2019s Cooperative Research Centres Program. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the US Government. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Available on ORBilu :

since 14 November 2024

Statistics

Number of views

182 (1 by Unilu)

Number of downloads

145 (0 by Unilu)

More statistics

Scopus citations^®

144

Scopus citations^®
without self-citations

129

OpenCitations

OpenAlex citations

178

WoS citations^™

118

Bibliography

D. Gašević G. Siemens S. Sadiq Empowering learners for the age of artificial intelligence Comput. Educ. Artif. Intell. 2023 4 100130
L. Yan et al. Practical and ethical challenges of large language models in education: a systematic scoping review Br. J. Educ. Technol. 2023 35 90 112
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).
Y. Li et al. Can large language models write reflectively Comput. Educ. Artif. Intell. 2023 4 100140
S.N. Yildirim-Erbasli O. Bulut Conversation-based assessment: a novel approach to boosting test-taking effort in digital formative assessment Comput. Educ. Artif. Intell. 2023 4 100135
C.A. Mazzoli F. Semeraro L. Gamberini Enhancing cardiac arrest education: exploring the potential use of Midjourney. Resuscitation 2023 189 109893
H. Vartiainen M. Tedre Using artificial intelligence in craft education: crafting with text-to-image generative models Digit. Creat. 2023 34 1 21
E. Kasneci et al. ChatGPT for good? On opportunities and challenges of large language models for education Learn. Individ. Diff. 2023 103 102274
T.P. Falcão R.F. Mello R.L. Rodrigues Applications of learning analytics in Latin America. J. Learn. Anal. 2020 51 871 874
A. Darvishi H. Khosravi S. Sadiq D. Gašević G. Siemens Impact of AI assistance on student agency. Comput. Educ. 2024 210 104967
E. Mousavinasab et al. Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods Interact. Learn. Environ. 2021 29 142 163
Vygotsky, L. S. & Cole, M. Mind in Society: Development of Higher Psychological Processes (Harvard Univ. Press, 1978).
S. Joksimovic D. Ifenthaler R. Marrone M. De Laat G. Siemens Opportunities of artificial intelligence for supporting complex problem-solving: findings from a scoping review. Comput. Educ. Artif. Intell. 2023 4 100138
Y. Chang et al. A survey on evaluation of large language models ACM Trans. Intell. Syst. Technol. 2024 15 1 45
Meet Khanmigo: Khan Academy’s AI-powered teaching assistant & tutor. Khan Academyhttps://www.khanmigo.ai/ (2023).
V.S. Lee What is inquiry-guided learning? New Dir. Teach. Learn. 2012 129 5 14
C.K.Y. Chan W. Hu Students’ voices on generative AI: perceptions, benefits, and challenges in higher education Int. J. Educ. Technol. High. Educ. 2023 20
S. Hennessy M. Cukurova C. Lewin M. Mavrikis L. Major BJET Editorial 2024: a call for research rigour Br. J. Educ. Technol. 2024 55 5 9
A. Darvishi H. Khosravi S. Sadiq D. Gašević G. Siemens Impact of AI assistance on student agency Comput. Educ. 2024 210 104967
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 https://doi.org/10.48550/arXiv.2407.09975 (2024).
I. Molenaar Towards hybrid human–AI learning technologies Eur. J. Educ. 2022 57 632 645
H. Ji I. Han Y. Ko A systematic review of conversational AI in language education: focusing on the collaboration with human teachers J. Res. Technol. Educ. 2023 55 48 63
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).
I. Pesovski R. Santos R. Henriques V. Trajkovik Generative AI for customizable learning experiences Sustainability 2024 16 3034
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).
T.K. Chiu The impact of generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interact. Learn. Environ. 2023 10.1080/10494820.2023.2253861
U. Lee et al. Prompt Aloud!: incorporating image-generative AI into STEAM class with learning analytics using prompt data. Educ. Inform. Technol. 2024 29 9575 9605
Y. Chen X. Zhang L. Hu A progressive prompt-based image-generative AI approach to promoting students’ achievement and perceptions in learning ancient Chinese poetry Educ. Technol. Soc. 2024 27 284 305
L. Long S. MacBlain M. MacBlain Supporting students with dyslexia at the secondary level: an emotional model of literacy J. Adolesc. Adult Lit. 2007 51 124 134
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).
S.O. Bada S. Olusegun Constructivism learning theory: a paradigm for teaching and learning J. Res. Method Educ. 2015 5 66 70
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).
A. Pardo J. Jovanovic S. Dawson D. Gašević N. Mirriahi Using learning analytics to scale the provision of personalised feedback Br. J. Educ. Technol. 2019 50 128 138
L.-A. Lim et al. What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course Learn. Instr. 2021 72 101202
J. Hattie H. Timperley The power of feedback Rev. Educ. Res. 2007 77 81 112
A. Poulos M.J. Mahony Effectiveness of feedback: the students’ perspective Assess. Eval. High. Educ. 2008 33 143 154
J. Steiss et al. Comparing the quality of human and ChatGPT feedback of students’ writing Learn. Instr. 2024 91 101894
J. Meyer 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. 2024 6 100199
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).
W. Wiboolyasarin K. Wiboolyasarin K. Suwanwihok N. Jinowat R. Muenjanchoey Synergizing collaborative writing and AI feedback: an investigation into enhancing L2 writing proficiency in Wiki-based environments Comput. Educ. Artif. Intell. 2024 6 100228
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).
W. Matcha D. Gašević A. Pardo et al. A systematic review of empirical studies on learning analytics dashboards: a self-regulated learning perspective IEEE Trans. Learn. Technol. 2019 13 226 245
M. Yang D. Carless The feedback triangle and the enhancement of dialogic feedback processes Teach. High. Educ. 2013 18 285 297
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).
J. McCarthy Evaluating written, audio and video feedback in higher education summative assessment tasks Issues Educ. Res. 2015 25 153 169
J. Orlando A comparison of text, voice, and screencasting feedback to online students Am. J. Distance Educ. 2016 30 156 166
M. Henderson M. Phillips Video-based feedback on student assessment: scarily personal. Austral. J. Educ. Technol. 2015 31 51 66
Z. Swiecki et al. Assessment in the age of artificial intelligence Comput. Educ. Artif. Intell. 2022 3 100075
Wu, Q. et al. AutoGen: enabling next-gen LLM applications via multi-agent conversation. Preprint at arXivhttps://doi.org/10.48550/arXiv.2308.08155 (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).
Y. Fan et al. Towards investigating the validity of measurement of self-regulated learning based on trace data Metacogn. Learn. 2022 17 949 987
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).
D. Gašević S. Greiff D.W. Shaffer Towards strengthening links between learning analytics and assessment: challenges and potentials of a promising new bond. Comput. Hum. Behav. 2022 134 107304
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).
R. Shahzad et al. Multi-agent system for students cognitive assessment in e-learning environment. IEEE Access 2024 12 15458 15467
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).
W. Song et al. An intelligent virtual standard patient for medical students training based on oral knowledge graph. IEEE Trans. Multimedia 2022 25 6132 6145
S. Ji S. Pan E. Cambria P. Marttinen S.Y. Philip A survey on knowledge graphs: representation, acquisition, and applications IEEE Trans. Neural Netw. Learn. Syst. 2021 33 494 514
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).
Q.-F. Yang L.-W. Lian J.-H. Zhao 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. 2023 20
B.N. Thanh et al. Race with the machines: assessing the capability of generative AI in solving authentic assessments Australas. J. Educ. Technol. 2023 39 59 81
N.C. Chesler et al. A novel paradigm for engineering education: virtual internships with individualized mentoring and assessment of engineering thinking J. Biomech. Eng. 2015 137 024701 25425046
R.P. Cant S.J. Cooper Simulation-based learning in nurse education: systematic review J. Adv. Nurs. 2010 66 3 15 20423432
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).
Z. Ji et al. Survey of hallucination in natural language generation ACM Comput. Surv. 2023 55 1 38
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 arXivhttps://doi.org/10.48550/arXiv.2302.03494 (2023).
M. Chelli et al. Hallucination rates and reference accuracy of ChatGPT and bard for systematic reviews: comparative analysis J. Med. Internet Res. 2024 26 e53164 38776130 11153973
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).
D.T.K. Ng J.K.L. Leung S.K.W. Chu M.S. Qiao Conceptualizing AI literacy: an exploratory review Comput. Educ. Artif. Intell. 2021 2 100041
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).
J. Schneider R. Richner M. Riser Towards trustworthy autograding of short, multi-lingual, multi-type answers Int. J. Artif. Intell. Educ. 2023 33 88 118
H. Khosravi et al. Explainable artificial intelligence in education Comput. Educ. Artif. Intell. 2022 3 100074
S.J. Yang H. Ogata T. Matsui N.-S. Chen Human-centered artificial intelligence in education: seeing the invisible through the visible Comput. Educ. Artif. Intell. 2021 2 100008
H. Short A critical evaluation of the contribution of trust to effective technology enhanced learning in the workplace: a literature review Br. J. Educ. Technol. 2014 45 1014 1022
C. Mutimukwe O. Viberg L.-M. Oberg T. Cerratto-Pargman Students’ privacy concerns in learning analytics: model development Br. J. Educ. Technol. 2022 53 932 951
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 arXivhttps://doi.org/10.48550/arXiv.2311.17035 (2023).
A. Winograd Loose-lipped large language models spill your secrets: the privacy implications of large language models. Harvard J. Law Technol. 2023 36 616 656
Y. Yao et al. A survey on large language model (LLM) security and privacy: the good, the bad, and the ugly. High Confid. Comput. 2024 4 100211
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).
L. Sha M. Raković A. Das D. Gašević G. Chen Leveraging class balancing techniques to alleviate algorithmic bias for predictive tasks in education IEEE Trans. Learn. Technol. 2022 15 481 492
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).
J. Wu 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 10.1155/2022/5378694
A. Tlili et al. What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education Smart Learn. Environ. 2023 10
EU AI act: first regulation on artificial intelligence. European Parliamenthttps://www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence (2023).
J. Mao B. Chen J.C. Liu Generative artificial intelligence in education and its implications for assessment. TechTrends 2023 68 58 66
Yang, Z. et al. AppAgent: multimodal agents as smartphone users. Preprint at arXiv https://doi.org/10.48550/arXiv.2312.13771 (2023).
O. Viberg M. Hatakka O. Bälter A. Mavroudi The current landscape of learning analytics in higher education Comput. Hum. Behav. 2018 89 98 110
G. Siemens et al. Human and artificial cognition Comput. Educ. Artif. Intell. 2022 3 100107
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).
R.A. Bjork J. Dunlosky N. Kornell Self-regulated learning: beliefs, techniques, and illusions Annu. Rev. Psychol. 2013 64 417 444 23020639
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 https://doi.org/10.48550/arXiv.2308.02312 (2023).
J. Rafner R.E. Beaty J.C. Kaufman T. Lubart J. Sherson Creativity in the age of generative AI. Nat. Hum. Behav. 2023 7 1836 1838 37985916
B. Shneiderman Human-centered artificial intelligence: reliable, safe & trustworthy Int. J. Hum. Comput. Interact. 2020 36 495 504
Giannini, S. Generative artificial intelligence in education: think piece by Stefania Giannini. unesco.org https://www.unesco.org/en/articles/generative-artificial-intelligence-education-what-are-opportunities-and-challenges (UNESCO, 2023).
T.H. Kung et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models PLoS Digit. Health 2023 2 e0000198 36812645 9931230
J.H. Choi K.E. Hickman A.B. Monahan D. Schwarcz ChatGPT goes to law school J. Leg. Educ. 2021 71 387
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 https://doi.org/10.48550/arXiv.2306.08997 (2023).
Chowdhuri, R., Deshmukh, N. & Koplow, D. No, GPT4 can’t ace MIT. Raunak Does Dev https://bit.ly/No-GPT4-can-t-ace-MIT (2023).
Lorenz, P., Perset, K. &; Berryhill, J. Initial Policy Considerations for Generative Artificial Intelligence (OECD, 2023).