Treffer: Exploring the Effects of Professor-Student Rapport, Grit and Motivation on Engagement in AI-Assisted English Classrooms: A Mixed-Methods Study from a Self-Determination Theory

Title:
Exploring the Effects of Professor-Student Rapport, Grit and Motivation on Engagement in AI-Assisted English Classrooms: A Mixed-Methods Study from a Self-Determination Theory
Language:
English
Authors:
Guanghui He (ORCID 0000-0001-5564-6704)
Source:
European Journal of Education. 2025 60(4).
Availability:
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed:
Y
Page Count:
13
Publication Date:
2025
Document Type:
Fachzeitschrift Journal Articles<br />Reports - Research
Education Level:
Higher Education
Postsecondary Education
Descriptors:
College Faculty, College Students, Teacher Student Relationship, Academic Persistence, Student Motivation, Second Language Learning, English (Second Language), Foreign Countries, Predictor Variables, Learner Engagement, Mediation Theory, Structural Equation Models, Artificial Intelligence
Geographic Terms:
China
DOI:
10.1111/ejed.70261
ISSN:
0141-8211
1465-3435
Entry Date:
2025
Accession Number:
EJ1490366
Database:
ERIC

Weitere Informationen

Guided by Self-Determination Theory, this mixed-methods study investigates how professor-student rapport, grit and motivation affect engagement among Chinese university students learning English. Quantitative data were collected from 721 students through self-report questionnaires, and qualitative data were obtained through semi-structured interviews with 20 learners. Quantitative data were analysed using SPSS 27.0 and AMOS 26.0, while qualitative interview data were examined through thematic analysis in NVivo. Structural Equation Modelling revealed that professor-student rapport significantly predicted grit, motivation and engagement. In addition, both grit and motivation positively predicted engagement, and mediation effects were observed. Interview findings further supported these relationships, highlighting the motivational and emotional processes underlying students' engagement in AI-supported learning. The study extends Self-Determination Theory into the context of AI-assisted language learning and offers practical implications for fostering student engagement through relational and motivational support in digitally enhanced classrooms.

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