Hybrid Human–AI Feedback in Technology-Enhanced Learning: Effects on Learner Motivation, Engagement, and Feedback Uptake

Recent advances in artificial intelligence are reshaping feedback practices in technology-enhanced learning environments. Learners increasingly receive feedback from both automated systems and human instructors, forming hybrid feedback ecosystems that blend AI’s speed and precision with educators’ interpretive and relational strengths. Despite wider adoption, little research has examined how learners interpret and respond to feedback from both sources. This qualitative study explores how hybrid human–AI feedback affects learner motivation, engagement, and uptake. Drawing on technology-enhanced learning, feedback as sense-making, attribution theory, and social presence theory, it investigates how learners evaluate feedback sources, assess credibility, and apply feedback in learning. The findings identify four themes: AI feedback sustains momentum through immediate guidance; human feedback adds contextual depth and reassurance; learners’ attribution of source influences trust and engagement; and hybrid feedback is most effective when AI and instructor roles are clearly defined and complementary. Overall, the study highlights that effective hybrid feedback relies not only on informational quality but also on learners’ perceptions of credibility and social presence. These insights contribute a socio-technical perspective on AI-supported education and offer implications for instructional design and future research on human–AI collaboration.

کلیدواژه ها:

Hybrid human–AI feedback ، Artificial intelligence in education ، Technology-enhanced learning ، Student motivation ، Student engagement ، Social presence ، Feedback perception ، Feedback uptake