AFFECTIVE AFFORDANCES IN AI-MEDIATED LEARNING: A SCOPING REVIEW ON ADOLESCENT MOTIVATION AND PSYCHOLOGICAL NEEDS
Abstract
This paper examines how the activation and promotion of positive emotions influence the learning experience of students aged 11 to 18 when interacting with artificial intelligence assistants in a school setting. The theoretical framework integrates Self-Determination Theory (Deci & Ryan, 2000), the Control-Value Theory of Achievement Emotions (Pekrun, 2006), the broaden-and-build theory (Fredrickson, 2004), and the concept of affordance (Gibson, 1979; Norman, 2013). The goal is to clarify how interaction design can promote emotions that are functional for learning, specifically epistemic curiosity, a sense of perceived competence, and relatedness. Using a critical review approach, this study involves the design of AI-mediated educational activities, differentiated by level of emotional support, in order to identify design principles capable of activating emotional affordance and fostering student engagement. The study aims to identify design principles for emotionally responsive educational systems, with theoretical and practical implications for the development of AI in schools.
References
Ahmed, A. (2024). Affective affordances in technology-mediated language teaching and learning. Technology in Language Teaching & Learning, 6(4), 1720. https://doi.org/10.29140/tltl.v6n4.1720
Alasgarova, R., & Rzayev, J. (2024). The Role of Artificial Intelligence in Shaping High School Students' Motivation. International Journal of Technology in Education and Science (IJTES), 8(2), 311-324. https://doi.org/10.46328/ijtes.553
Billingsley, B., Clarke, S., & Selker, T. (2025). EI Search: How to Search for Knowledge in the Age of Generative AI. Science & Education. https://doi.org/10.1007/s11191-025-00687-4. https://doi.org/10.1007/s11191-025-00687-4
Cai, H. (2025). Research on the Path of Empowering Ideological and Political Education with Generative Artificial Intelligence. Proceedings of the 2025 2nd International Conference on Artificial Intelligence and Future Education (AIFE 2025). 6 https://doi.org/10.1145/3785987.3786090
Chavez, O. J., & Palaoag, T. (2024). AI-driven mobile application: unraveling students’ motivational feature preferences for reading comprehension. Journal of Research in Innovative Teaching & Learning, 17(2), 226-242. https://doi.org/10.1108/jrit-02-2024-0045
Chiu, T. K. F., Bali, S., Tondeur, J., Howard, S., & Chan, K. K. H. (2026). Teacher education for artificial intelligence literacy through a self-determination theory perspective. European Journal of Teacher Education. https://doi.org/10.1080/02619768.2026.2621848
Choi, S., Jeon, J., & Jang, Y. (2025). Factors influencing teachers’ intention to teach AI based on SDT and TAM. Education and Information Technologies, 30, 24173–24200. https://doi.org/10.1007/s10639-026-14040-x
Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/s15327965pli1104_01
Demir, K., et al. (2025). Exploring Artificial Intelligence Tutor Teammate Adaptability to Harness Discovery Curiosity and Promote Learning in the Context of Interactive Molecular Dynamics. Cogn Comput 17, 143 (2025). https://doi.org/10.1007/s12559-025-10498-y
Fredrickson, B. L. (2004). The broaden-and-build theory of positive emotions. Philosophical Transactions of the Royal Society B: Biological Sciences, 359(1449), 1367–1377. https://doi.org/10.1093/acprof:oso/9780198567523.003.0008
Galindo‐Domínguez, H., Delgado, N., Urruzola, M. V., Etxabe, J. M., & Campo, L. (2025). Using artificial intelligence to promote adolescents' learning motivation. a longitudinal intervention from the self‐determination theory. Journal of Computer Assisted Learning, 41(2), e70020. https://doi.org/10.1111/jcal.70020
Garcia, D., Granjard, A., Vanhée, L., Berg, M., Andersson, G., Lasota, M., & Sikström, S. (2025). AI-driven analyzes of open-ended responses to assess outcomes of internet-based cognitive behavioral therapy (ICBT) in adolescents with anxiety and depression comorbidity. Journal of Affective Disorders, 381, 659–668. https://doi.org/10.1016/j.jad.2025.04.003
Gibson, J. J. (1979). The ecological approach to visual perception. Houghton Mifflin. https://doi.org/10.1002/bs.3830260313
Han, J., Liu, G. & Xiang, S. To engage with AI or not: learning engagement among rural junior high school students in an AI-powered adaptive learning environment. Humanit Soc Sci Commun 12, 1292 (2025). https://doi.org/10.1057/s41599-025-05676-0
Hmoud, H., & Al-Adwan, A. (2025). Who Thrives with AI? A Moderation Analysis of the Impact of AI-Based Learning on Students’ Subjective Wellbeing by Learning Style? 2025 International Conference on Smart Learning Courses (SCME), pp. 81-87. https://doi.org/10.1109/scme62582.2025.11104892
Hsieh, Y. C. (2024). Multimodal Digital Storytelling Presentations among Middle School Learners of English as a Foreign Language: Emotions, Grit and Perceptions. Relc Journal, 55(2), 547-558. https://doi.org/10.1177/00336882221102233
Jarske, S., Kaipainen, K., & Väänänen, K. (2024). Exploration of Social Robots As Climate Communicators to Foster Climate Hope in Adolescents. Social Robots with AI: Prospects, Risks, and Responsible Methods: Proceedings of Robophilosoph, 18(1), 383–395. https://doi.org/10.3233/faia241526
Kang, X., Yu, E., Chen, K., Sang, W., Lai, X., Lin, P., & Sun, P. (2023). A novel active-learning based emotion-vision-transformer network for expression recognition. 2023 4th International Symposium on Artificial Intelligence for Medicine Science (ISAIMS 2023). 1-8. https://doi.org/10.1145/3644116.3644194
Lajoie, S. P., Pekrun, R., Azevedo, R., & Leighton, J. P. (2020). Understanding and measuring emotions in technology-rich learning environments. Learning and Instruction, 70, Article 101212. https://doi.org/10.1016/j.learninstruc.2019.101272
Lim, K. Y. T., Wong, Y. H., Tran, D. N., Lee, E. K. X., Nguyen, T. M. T., Nguyen, D. M. A., & Tan, A. J. H. (2026). In Situ Analysis of Electrodermal Activity from Students Learning from Large Language Models Versus Curated Texts. Brain Sciences, 16, 153. https://doi.org/10.3390/brainsci16020153
Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 70, Article 101202. https://doi.org/10.1016/j.learninstruc.2018.08.002
Norman, D. A. (2013). The design of everyday things (Rev. and expanded ed.). Basic Books. https://doi.org/10.15358/9783800648108
Page, M. J., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.7717/peerj.20819/supp-1
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.4324/9780203148211.ch7
Pekrun, R. (2018). Control-value theory: A social-cognitive approach to achievement emotions. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 120–141). Routledge. https://doi.org/10.1108/978-1-64113-270-120251011
Reschly, A. L., Huebner, E. S., Appleton, J. J., & Antaramian, S. (2008). Engagement as flourishing: The contribution of positive emotions and coping to adolescents' engagement at school and with learning. Psychology in the Schools, 45(5), 419-431. https://doi.org/10.1002/pits.20306
Roiniță, A. (2026). Human touch versus algorithm: reception of AI poetry among Romanian adolescents. Frontiers in Education, 11, 1728281. https://doi.org/10.3389/feduc.2026.1728281
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037//0003-066x.55.1.68
Santana-Monagas, E., Núñez, J. L., & León, J. (2025). Teacher empathy messages: The role of teacher enthusiasm and student outcomes. British Journal of Educational Psychology, 95(Suppl. 1), S52–S72. https://doi.org/10.1111/bjep.12766
Sim, S. S. Y., Lau, B. T., Islam, F. A., & Than, J. C. M. (2024). An artificial intelligence curriculum for secondary school students in Malaysia: Study protocol. In Proceedings of the IEEE International Conference on Engineering Education (ICEED) (pp. 507–512). IEEE. https://doi.org/10.1109/iceed62316.2024.10923828
Sivenas, T. (2025). ChatGPT-5 in Secondary Education: A Mixed-Methods Analysis of Student Attitudes, AI Anxiety, and Hallucination-Aware Use. arXiv preprint arXiv:2512.04109. https://doi.org/10.48550/arXiv.2512.04109
Soldatova, G. U., Chigarkova, S. V., & Ilyukhina, S. N. (2025). The Digital Centaur as a Type of Technologically Augmented Human in the AI Era: Personal and Digital Predictors. Behavioral Sciences, 15(11), 1487. https://doi.org/10.3390/bs15111487
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., ... & Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of internal medicine, 169(7), 467-473.
Vanhoffelen, G., Vandenbosch, L., & Schreurs, L. (2025). Teens, tech, and talk: adolescents’ use of and emotional reactions to Snapchat’s my AI chatbot. Behavioral Sciences, 15(8), 1037. https://doi.org/10.20944/preprints202505.1885.v1
Xia, Q., Chiu, T. K., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022). A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education. Computers & education, 189, 104582. https://doi.org/10.1016/j.compedu.2022.104582
Yin Sim, L., et al. (2026). Inclusive education and flexible interface design: A multiple case study on AI environments for neurodivergent adolescents in the Mediterranean area. 2024 IEEE 13th International Conference on Engineering Education (ICEED). pp. 1-5. https://doi.org/10.1109/iceed62316.2024.10923828
Zarkadoulas, N., & Virvou, M. (2025). Enhancing Mathematical Confidence and Reducing Math Anxiety Via Ai: a Factor Analysis of High School Students' Interaction With Algebra Chatbots. 16th International Conference on Information, Intelligence, Systems & Applications. 1-6. https://doi.org/10.1109/iisa66859.2025.11311296
Zheng, S. (2024). The effects of chatbot use on foreign language reading anxiety and reading performance among Chinese secondary school students. Computers and Education: Artificial Intelligence, 7, 100271. https://doi.org/10.1016/j.caeai.2024.100271
DOI: https://doi.org/10.32043/gsd.v10i4.1898
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 ITALIAN JOURNAL OF HEALTH EDUCATION, SPORT AND INCLUSIVE DIDACTICS

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Italian Journal of Health Education, Sports and Inclusive Didactics
Publisher: Cares srl – REN Press (since 2026). Previously published by Gaia srl – Edizioni Universitarie Romane (2017–2025).
ISSN: 2532-3296 E-ISSN: 3103-5744
Italian Journal of Health Education, Sports and Inclusive Didactics
