Generative AI Use and Learning Productivity Among Google Gemini Users in Bangladesh: An Integrated TAM-Self-Regulated Learning Model

Generative artificial intelligence (GenAI) is increasingly embedded in students’ learning and knowledge-work routines, yet perceived productivity gains do not necessarily arise from technology acceptance alone. This study develops and empirically examines an integrated Technology Acceptance Model (TAM) and Self-Regulated Learning (SRL) framework for explaining learning productivity among Google/Gemini users in Bangladesh. The study dataset contains 400 respondent IDs, of which 385 records contain complete questionnaire responses and a valid attention-check response; 15 rows are blank. The analysis therefore uses 385 usable cases and conducts an additional sensitivity analysis on 302 respondents classified as regular or occasional AI-feature users. Perceived ease of use (PEOU), perceived usefulness (PU), SRL and learning productivity (LP) were operationalised using three five-point Likert items each. Transparent composite-score path analysis with 5,000 bootstrap resamples was used because the available analytical environment did not contain a dedicated CFA/PLS-SEM implementation; consequently, the reported measurement diagnostics are explicitly treated as pre-publication validation evidence rather than as a claim of completed covariance-based CFA or PLS-SEM. PEOU positively predicted PU (β = .278, p < .001), while both PEOU (β = .191, p < .001) and PU (β = .218, p < .001) predicted SRL. Learning productivity was predicted by PEOU (β = .140, p = .004), PU (β = .256, p < .001) and SRL (β = .202, p < .001), with R² = .188. Bootstrap estimates also supported indirect pathways from PEOU to LP through PU and SRL, including a serial PEOU → PU → SRL → LP effect. The results suggest that the productivity value of GenAI is partly behavioural: users benefit not only because the technology is perceived as useful and easy to operate, but also because those perceptions are associated with planning, monitoring and addressing learning gaps. The study contributes an emerging-market, mechanism-oriented account of GenAI-enabled learning productivity and identifies measurement and sampling issues that should be resolved in subsequent validation research.