Teachers’ Interpretations of Learning Engagement in the Artificial Intelligence Era: A Qualitative Model
The objective of this study was to explore how teachers interpret learning engagement in AI-mediated classrooms and develop a qualitative model based on their lived professional experiences. This qualitative study used a phenomenological–interpretive design to explore teachers’ conceptualizations of engagement in the context of artificial intelligence. Data were collected through semi-structured, in-depth interviews with 24 teachers from Tehran selected via purposive sampling to ensure variation in teaching level, experience, and familiarity with AI-supported tools. Interviews lasted 45–75 minutes, were audio-recorded, transcribed verbatim, and analyzed using thematic analysis supported by NVivo software. Coding proceeded through open, axial, and selective stages, and theoretical saturation was achieved after 24 interviews. Trustworthiness was ensured through peer debriefing, constant comparison, and analytic memoing. Analysis yielded four overarching themes: (1) redefinition of learning engagement, (2) AI as a catalyst for instructional transformation, (3) challenges and tensions in AI-supported engagement, and (4) shifts in teacher professional identity. Teachers reported that AI reshapes cognitive, emotional, and behavioral facets of engagement through adaptive feedback, analytics, and interactive tools. AI-personalized pathways strengthened perceived cognitive engagement, while real-time data influenced how teachers inferred students’ attention and persistence. However, concerns emerged regarding algorithmic accuracy, over-reliance on automation, and pedagogical misalignment. Teachers also described evolving roles as facilitators of human–AI interaction, interpreting engagement through both professional judgment and AI signals. These inferential results indicate that teacher interpretations are hybrid, negotiated, and context-dependent. Teachers in AI-enhanced classrooms reconceptualize learning engagement as a multidimensional and technology-mediated construct shaped by analytics, emotional responses, behavioral cues, and evolving professional identities. The resulting qualitative model underscores the need for AI literacy, critical interpretation of automated insights, and balanced integration of human expertise and intelligent systems.