AI-generated characters for supporting personalized learning and well-being
Open accessAbstract
Advancements in machine learning have recently enabled the hyper-realistic synthesis of prose, images, audio and video data, in what is referred to as artificial intelligence (AI)-generated media. These techniques offer novel opportunities for creating interactions with digital portrayals of individuals that can inspire and intrigue us. AI-generated portrayals of characters can feature synthesized faces, bodies and voices of anyone, from a fictional character to a historical figure, or even a deceased family member. Although negative use cases of this technology have dominated the conversation so far, in this Perspective we highlight emerging positive use cases of AI-generated characters, specifically in supporting learning and well-being. We demonstrate an easy-to-use AI character generation pipeline to enable such outcomes and discuss ethical implications as well as the need for including traceability to help maintain trust in the generated media. As we look towards the future, we foresee generative media as a crucial part of the ever growing landscape of human–AI interaction. Digitally recreating the likeness of a person used to be a costly and complex process. Through the use of generative models, AI-generated characters can now be made with relative ease. Pataranutaporn et al. discuss in this Perspective how this technology can be used for positive applications in education and well-being.
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This can significantly reduce work-related stress, allowing teachers to manage their time and workloads better and enjoy a healthier work-life balance.
“This can significantly reduce work-related stress, allowing teachers to manage their time and workloads better and enjoy a healthier work-life balance (Pataranutaporn et al., 2021).”
From Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania (2024) · cites this paper as Pataranutaporn et al., 2021
Recent advances in technology for hyper-realistic manipulations to video pose a threat to traditional standards of evidence and trust in media.
“Recent advances in technology for algorithmically applying hyper-realistic manipulations to video are simultaneously enabling new forms of interpersonal communication and posing a threat to traditional standards of evidence and trust in media1–8.”
From Human detection of political speech deepfakes across transcripts, audio, and video (2024) · cites this paper as [6]
The illusion of consensuality generated by AI characters collapses under scrutiny.
“The illusion of consensuality, generated by AI characters trained to affirm and adapt to user desire, collapses under scrutiny”
From Toward an Ethic of Synthetic Relationality: Identity, Intimacy, and Risk in AI-Mediated Roleplay Environments (2025) · cites this paper as Pataranutaporn et al. 2021
AI systems do not understand harm, only input-output optimization.
“these systems do not understand harm, only input-output optimization (Pataranutaporn et al. 2021).”
From Toward an Ethic of Synthetic Relationality: Identity, Intimacy, and Risk in AI-Mediated Roleplay Environments (2025) · cites this paper as Pataranutaporn et al. 2021
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References 64 · Cited by 358
- Authors
- Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M.
- Affiliations
- Massachusetts Institute of Technology (US)
- The University of Osaka (JP)
- University of California, Santa Barbara (US)
- Corresponding author
- Sra, Misha
- ORCID
- Venue
- Nature Machine Intelligence
- Published
- 2021-12-15
- Volume
- 3
- Issue
- 12
- Pages
- 1013-1022
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W4200295417
- Cited by
- 358
- References
- 64
- Citations by year
- 2026: 60 · 2025: 114 · 2024: 120 · 2023: 55 · 2022: 9
- FWCI
- 41.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion, public-domain)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- 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
- generative media, personalized learning, well-being, human-AI interaction, ethical implications, traceability, educational technology
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