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Blending Generative AI and Instructor-Led Learning: Empirical Insights on Student Motivation, Learning Experience, and Academic Performance in Higher Education

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Publicado en:Education Sciences vol. 15, no. 11 (2025), p. 1480-1498
Autor principal: Dizza, Beimel
Otros Autores: Amzalag Meital, Zviel-Girshin Rina, Voloch Nadav
Publicado:
MDPI AG
Materias:
Problem solving
Teaching
Pedagogy
Higher education
Students
Generative artificial intelligence
Teachers
Chatbots
Motivation
Academic achievement
Skill development
Metacognition
Self-efficacy
Learning
Quantitative analysis
Learning Modalities
Literature Reviews
Competence
Instructional Innovation
Learning Motivation
Influence of Technology
Conventional Instruction
Active Learning
Learning Strategies
Learning Processes
Learning Experience
Personal Autonomy
Individualized Instruction
Goal Orientation
Blended Learning
Artificial Intelligence
In Person Learning
Student Motivation
Outcomes of Education
Opportunities
Learner Engagement
Classroom Environment
Acceso en línea:Citation/Abstract
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Resumen:The growing integration of generative artificial intelligence (GenAI) tools in higher education has potential to transform learning experiences. However, empirical research comparing GenAI-supported learning with traditional instruction lags behind these developments. This study addresses this gap through a controlled experiment involving 96 undergraduate computer science students in a Database Management course. Participants experienced either GenAI-supported or traditional instructions while learning the same concept. Data were collected through questionnaires, quizzes, and interviews. Analyses were grounded in self-determination theory (SDT), which posits that effective learning environments support autonomy, competence, and relatedness. Quantitative findings revealed significantly more positive learning experiences with GenAI tools, particularly enhancing autonomy through personalized pacing and increased accessibility. Competence was supported, reflected in shorter study times with no significant achievement differences between approaches. Students performed better on moderately difficult questions using GenAI, indicating that GenAI may bolster conceptual understanding. However, interviews with 11 participants revealed limitations in supporting relatedness. While students appreciated GenAI’s efficiency and availability, they preferred instructor-led sessions for emotional engagement and support with complex problems. This study contributes to the theoretical extension of SDT in technology-mediated learning contexts and offers practical guidance for optimal GenAI integration.
ISSN:2227-7102
2076-3344
DOI:10.3390/educsci15111480
Fuente:Education Database