AI-Mediated Control–Value Appraisal and Emotional Experiences: A Socio-Technical Extension of Generalized Control–Value Theory
DOI: 10.23977/appep.2026.070104 | Downloads: 0 | Views: 7
Author(s)
Yu Liu 1
Affiliation(s)
1 Golden Gate University, San Francisco, CA, 94105, USA
Corresponding Author
Yu LiuABSTRACT
In the era of AI-assisted learning, academic attention to learners' emotional experiences has rapidly increased. Existing research primarily views AI as an external influence (e.g., a risk, motivator, or facilitator), and focuses mainly on individual-level cognitive mechanisms. However, few studies have addressed how AI-assisted environments shape the construction of control and value appraisals underlying emotional experiences. This paper proposes that AI-assisted environments play a structural mediating role in the construction of control and value appraisals, thus extending Generalized Control-Value Theory (GCVT) to the socio-technical field. Specifically, we argue that AI influences these assessments by introducing distributed agency, AI-mediated expectations, and AI-assisted cognition into the formation of control appraisal and the structural reorganization of intrinsic and extrinsic value appraisal. Combining evidence from AI research, cognitive science, and experimental studies, we propose a comprehensive conceptual framework that elucidates how AI contributes to the construction of control and value appraisals, ultimately influencing emotional experiences. This framework positions AI as part of the emotion generating mechanism rather than merely an external situational factor, thus deepening our understanding of emotions in AI-driven digital environments and making it more contextually sensitive and socio-technically grounded. Furthermore, this framework provides theoretical insights for educational psychology and points to future research directions for AI-supported learning systems.
KEYWORDS
Generalized control-value theory (GCVT), artificial intelligence (AI), appraisal construction, emotional experiencesCITE THIS PAPER
Yu Liu. AI-Mediated Control–Value Appraisal and Emotional Experiences: A Socio-Technical Extension of Generalized Control–Value Theory. Applied & Educational Psychology (2026). Vol. 7, No.1, 21-28. DOI: http://dx.doi.org/10.23977/appep.2026.070104.
REFERENCES
[1] Zhang, S., Meng, Z., Chen, B., Yang, X., & Zhao, X. (2021). Motivation, social emotion, and the acceptance of artificial intelligence virtual assistants—Trust-based mediating effects. Frontiers in Psychology, 12, 728495. https://doi.org/10.3389/fpsyg.2021.728495
[2] Santiago-Torner, C., Corral-Marfil, J. A., & Tarrats-Pons, E. (2025). Artificial Intelligence and the Reconfiguration of Emotional Well-Being (2020–2025): A Critical Reflection. Societies, 16(1), 6. https://doi.org/10.61336/jmsr/25-04-28
[3] Chavan, V., Cenaj, A., Shen, S., Bar, A., Binwani, S., Del Becaro, T., ... & Fresquet, X. (2025). Feeling machines: ethics, culture, and the rise of emotional AI. arXiv preprint arXiv:2506.12437. https://doi.org/10.48550/arXiv.2506.12437
[4] Su, F., Liu, W., Xiong, K., & Zeng, Q. (2024). How and when artificial intelligence usage facilitates task performance. Social Behavior and Personality: an international journal, 52(10), 1-11. https://doi.org/10.2224/sbp.13634
[5] Vinichenko, M. V., Melnichuk, A. V., & Karácsony, P. (2020). Technologies of improving the university efficiency by using artificial intelligence: Motivational aspect. Entrepreneurship and sustainability issues, 7(4), 2696. https://doi.org/10.1007/978-3-031-81962-9_58
[6] Naseer, A., Ahmad, N. R., & Chishti, M. A. (2025). Psychological impacts of AI dependence: Assessing the cognitive and emotional costs of intelligent systems in daily life. Review of Applied Management and Social Sciences, 8(1), 291-307. https://doi.org/10.47067/ramss.v8i1.458
[7] Khassawneh, O., Mohammad, T., & Tabche, I. (2026). Delegating to the algorithm: how AI-driven leadership shapes bias perceptions, trust and emotional disengagement in the workplace. Leadership & Organization Development Journal, 1-18. https://doi.org/10.1108/LODJ-06-2025-0406
[8] Pekrun, R. Control-Value Theory: From Achievement Emotion to a General Theory of Human Emotions. Educational Psychology Review, 36, 83 (2024). https://doi.org/10.1007/s10648-024-09909-7
[9] Pekrun, R. The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice. Educational Psychology Review, 18, 315–341 (2006). https://doi.org/10.1007/s10648-006-9029-9
[10] Pekrun, R., and Perry, R. P. (2014). “Control-value theory of achievement emotions,” in International Handbook of Emotions in Education, eds L. Linnenbrink-Garcia and R. Pekrun (New York, NY: Taylor & Francis), 120–141.
[11] Pekrun, R. (2018). Control-value theory: A social-cognitive approach to achievement emotions. In G. A. D. Liem & D. M. McInerney (Eds.), Big theories revisited 2: A volume of research on sociocultural influences on motivation and learning (pp. 162–190). Information Age Publishing.
[12] Pekrun, R. (2014). Emotions and learning (Educational Practices Series, Vol. 24). International Academy of Education (IAE) and International Bureau of Education (IBE) of the United Nations Educational, Scientific and Cultural Organization (UNESCO), Geneva, Switzerland. http://www.iaoed.org/downloads/edu-practices_24_eng.pdf
[13] Faas, C., Bergs, R., Sterz, S., Langer, M., & Feit, A. M. (2024). Give Me a Choice: The Consequences of Restricting Choices Through AI-Support for Perceived Autonomy, Motivational Variables, and Decision Performance (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2410.07728
[14] Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684
[15] Vodiškar, M., & Ruiner, C. (2025). Collaboration between individuals and AI: fusing mental effort and AI for work meaningfulness. AI & Society. https://doi.org/10.1007/s00146-025-02772-2
[16] Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, 104967. https://doi.org/10.1016/j.compedu.2023.104967
[17] Luo, L., & Yusuf, A. (2025). Bridging AI and pedagogy: how AI-adaptive feedback shapes Chinese EFL students’ writing engagement, metacognitive writing strategies, and writing performance. Assessment Evaluation in Higher Education, 1–24. https://doi.org/10.1080/02602938.2025.2548919
[18] Coenen, C., & Pfenninger, M. (2024). Transforming learning experiences and assessments through AI‐empowered cocreation of quality feedback. New Directions for Teaching and Learning, 2025(182), 59–65. https://doi.org/10.1002/tl.20628
[19] Tavares, T. F., & Soares, L. P. (2025). Automated Formative Feedback for Short-form Writing: An LLM-Driven Approach and Adoption Analysis (Version 1). arXiv.https://doi.org/10.48550/ARXIV.2509.22734
[20] Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2024). Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance. arXiv. https://doi.org/10.48550/ARXIV.2412.09315
[21] Lin, Y.-L., Wang, W.-T., & Hsieh, M.-J. (2024). The effects of students’ self-efficacy, self-regulated learning strategy, perceived and actual learning effectiveness: A digital game-based learning system. Education and Information Technologies, 29(16), 22213–22245. https://doi.org/10.1007/s10639-024-12700-4
[22] Xu, Q., Liu, Y., & Li, X. (2025). Unlocking student potential: How AI-driven personalized feedback shapes goal achievement, self-efficacy, and learning engagement through a self-determination lens. Learning and Motivation, 91, 102138.
[23] Nguyen-Viet, B., & Doan, H. (2026). Integrating gamification and artificial intelligence in higher education: a self-determination theory approach to motivation and learning effectiveness. Asian Education and Development Studies, 1-18.
[24] Robert H. Thomson, Edward A. Cranford, Gabe Tucker, and Christian Lebiere "Comparison of cognitively-inspired salience and feature importance techniques in intrusion detection datasets", Proc. SPIE 13054, Assurance and Security for AI-enabled Systems, 130540N (7 June 2024). https://doi.org/10.1117/12.3013842
[25] Kausar, F. N., Shakir, F., & Aziz, K. (2024). Effect of Artificial Intelligence Usage on Students' Motivation toward Learning at University Level. Journal of Social Signs Review, 2(4), 307-323.
[26] Persson, J. (2024). Artificial Intelligence and UK Education: Research, the Redistribution of Authority, and Rights. International Journal of Artificial Intelligence in Education, 34(1), 62-72.
https://doi.org/10.1007/s40593-023-00347-0
[27] Brusilovsky, P. (2024). AI in education, learner control, and human-AI collaboration. International Journal of Artificial Intelligence in Education, 34(1), 122-135. https://doi.org/10.1007/s40593-023-00356-z
[28] Snyder, M. L., Kleck, R. E., Strenta, A., & Mentzer, S. J. (1979). Avoidance of the handicapped: an attributional ambiguity analysis. Journal of personality and social psychology, 37(12), 2297–2306. https://doi.org/10.1037//0022-3514.37.12.2297
[29] Chen, J., He, M., & Sun, J. (2025). AI anxiety and knowledge payment: the roles of perceived value and self-efficacy. BMC psychology, 13(1), 208. https://doi.org/10.1186/s40359-025-02510-9
[30] Chen, Y., Wang, Y., Wüstenberg, T., Kizilcec, R. F., Fan, Y., Li, Y., Lu, B., Yuan, M., Zhang, J., Zhang, Z., Geldsetzer, P., Chen, S., & Bärnighausen, T. (2025). Effects of generative artificial intelligence on cognitive effort and task performance: Study protocol for a randomized controlled experiment among college students. Trials, 26(1), 244. https://doi.org/10.1186/s13063-025-08950-3
[31] Tao, W., Zhang, M., & Liu, Y. (2024). Mastering delegation to artificial intelligence creative tools: The concept, dimensions, and development of a scale to measure cognitive outsourcing. Social Behavior and Personality: an international journal, 52(12), 1-15.
[32] Wu, S., Liu, Y., Ruan, M., Chen, S., & Xie, X. Y. (2025). Human-generative AI collaboration enhances task performance but undermines human’s intrinsic motivation. Scientific Reports, 15(1), 15105. https://doi.org/10.1038/s41598-025-98385-2
[33] Varghese, J. (2025). The Impact of Artificial Intelligence on Critical Thinking, Epistemic Curiosity and Epistemic Autonomy in the Context of Education. Symposion, 12(2), 309-326. https://doi.org/10.5840/symposion202512218
[34] Chan, J., Choi, F., Saha, K., & Chandrasekharan, E. (2025). The Ranking Effect: How Algorithmic Rank Influences Attention on Social Media. arXiv preprint. https://doi.org/10.48550/arXiv.2509.18440
[35] Ghazarian, S., Weischedel, R., Galstyan, A., & Peng, N. (2020, April). Predictive engagement: An efficient metric for automatic evaluation of open-domain dialogue systems. In Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), pp. 7789-7796. https://doi.org/10.48550/arXiv.1911.01456
[36] Brady, W. J., Jackson, J. C., Lindström, B., & Crockett, M. J. (2023). Algorithm-mediated social learning in online social networks. Trends in cognitive sciences, 27(10), 947-960. https://doi.org/10.1016/j.tics.2023.06.008
[37] Chang, J. P.-C., Cheng, S.-W., Chang, S. M.-J., & Su, K.-P. (2025). Navigating the Digital Maze: A Review of AI Bias, Social Media, and Mental Health in Generation Z. AI, 6(6), 118. https://doi.org/10.3390/ai6060118
[38] Formosa, P., Bankins, S., Matulionyte, R., & Ghasemi, O. (2025). Can ChatGPT be an author? Generative AI creative writing assistance and perceptions of authorship, creatorship, responsibility, and disclosure. AI & Society, 40(5), 3405-3417. https://doi.org/10.1007/s00146-024-02081-0
[39] Sankaran, S., Zhang, C., Aarts, H., & Markopoulos, P. (2021). Exploring Peoples' Perception of Autonomy and Reactance in Everyday AI Interactions. Frontiers in psychology, 12, 713074. https://doi.org/10.3389/fpsyg.2021.713074
[40] Legaspi, R., Xu, W., Konishi, T., Wada, S., Kobayashi, N., Naruse, Y., & Ishikawa, Y. (2024). The sense of agency in human–AI interactions. Knowledge-Based Systems, 286, 111298. https://doi.org/10.1016/j.knosys.2023.111298
[41] Villa, S., Barth, L. L., Chiossi, F., Welsch, R., & Kosch, T. (2025). Whose mind is it anyway? A systematic review and exploration on agency in cognitive augmentation. Computers in Human Behavior: Artificial Humans, 5, 100158. https://doi.org/10.1016/j.chbah.2025.100158
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