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The Evolution of Learners' Interaction, Cognition, and Emotion in AI Digital Human-Mediated Learning

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DOI: 10.23977/appep.2025.060217 | Downloads: 7 | Views: 225

Author(s)

Yang Yang 1

Affiliation(s)

1 International Department, Guangzhou College of Commerce, Guangzhou, China

Corresponding Author

Yang Yang

ABSTRACT

This study explores how learners' interactional relationships, cognitive engagement, and emotional responses evolve through sustained interaction with an AI digital human in a vocational English writing course. A qualitative case study was conducted over six weeks, during which twelve students engaged regularly with an AI agent named Ava that provided feedback, prompts, and revision support. Data were collected from semi-structured interviews, weekly reflection journals, and system interaction logs. Thematic analysis revealed three core developmental trajectories: (1) Interactional Repositioning, where learners shifted from viewing Ava as a tool to perceiving her as a dialogic partner; (2) Emotional Realignment, marked by a transition from discomfort or skepticism to trust and empathy; and (3) Cognitive Reframing, where learners moved from surface-level editing to metacognitive writing awareness. These findings suggest that AI digital humans can play a relational role in shaping learners’ emotional receptiveness and strategic thinking, particularly when interactions are consistent and socially embedded. The study contributes to a learner-centered understanding of AI-mediated learning and highlights the need to consider affective and cognitive development as intertwined processes in AI-supported education.

KEYWORDS

AI Digital Human, Learner-AI Interaction, Emotional Engagement, Metacognitive Development, Qualitative Case Study

CITE THIS PAPER

Yang Yang, The Evolution of Learners' Interaction, Cognition, and Emotion in AI Digital Human-Mediated Learning. Applied & Educational Psychology (2025) Vol. 6: 129-136. DOI: http://dx.doi.org/10.23977/appep.2025.060217.

REFERENCES

[1] Ayeni, O.O., Al Hamad, N.M., Chisom, O.N., Osawaru, B. and Adewusi, O.E. (2024) AI in education: A review of personalized learning and educational technology. GSC Advanced Research and Reviews, 18(2), 261–271.
[2] Braun, V. and Clarke, V. (2006) Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
[3] Carless, D. and Boud, D. (2018) The development of student feedback literacy: enabling uptake of feedback. Assessment and Evaluation in Higher Education, 43(8), 1315–1325.
[4] Dell’Aquila, E., Ponticorvo, M. and Limone, P. (2025) Psychological Foundations for Effective Human–Computer Interaction in Education. Applied Sciences-Basel, 15(6).
[5] Gu, J.Q. (2024) Application of Digital Human Technology in Vocational College English Teaching: Enhancing Student Learning Experience and Practical Skills. 8th International Conference on E-Society, E-Education and E-Technology, 1–7.
[6] Liu, Y., Nie, X. and Wu, Z. (2024) Collaboration of Digital Human Gestures and Teaching Materials for Enhanced Integration in MOOC Teaching Scenarios. International Conference on Human-Computer Interaction, 169–175.
[7] Mohammed, S.J. and Khalid, M.W. (2025) Under the world of AI-generated feedback on writing: mirroring motivation, foreign language peace of mind, trait emotional intelligence, and writing development. Language Testing in Asia, 15(1), 7.
[8] Mossbridge, J. (2024) Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model. arXiv preprint arXiv:2410.11864.
[9] Krashen, S.D. (1982) Principles and practice in second language acquisition. Pergamon Press.
[10] Vygotsky, L.S. (1978) Mind in society: The development of higher psychological processes. Harvard University Press.
[11] Wang, X., Pang, H., Wallace, M.P., Wang, Q. and Chen, W. (2024) Learners’ perceived AI presences in AI-supported language learning: A study of AI as a humanized agent from community of inquiry. Computer Assisted Language Learning, 37(4), 814–840.
[12] Wood, J. (2023) Enabling feedback seeking, agency and uptake through dialogic screencast feedback. Assessment and Evaluation in Higher Education, 48(4), 464–484.
[13] Zhou, Y., Xu, K., Yin, B. and Liu, N. (2024) Research on the Application of Digital Humans in English Oral Teaching Based on AI Models. International Conference on Distance Education and Learning.

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