This exploratory research conducted a thematic analysis of students’ experiences and utilization of AI tools by students in educational settings. We surveyed 87 undergraduates from two different educational courses at a comprehensive university in Western Canada. Nine integral themes that represent AI’s role in student learning and key issues with respect to AI have been identified. The study yielded three critical insights: the potential of AI to expand educational access for a diverse student body, the necessity for robust ethical frameworks to govern AI, and the benefits of personalized AI-driven support. Based on the results, a model is proposed along with recommendations for an optimal learning environment, where AI facilitates meaningful learning. We argue that integrating AI tools into learning has the potential to promote inclusivity and accessibility by making learning more accessible to diverse students. We also advocate for a shift in perception among educational stakeholders towards AI, calling for de-stigmatization of its use in education. Overall, our findings suggest that academic institutions should establish clear, empirical guidelines defining student conduct with respect to what is considered appropriate AI use.

Past research shows that both teachers’ technological pedagogical content knowledge (TPACK) and their engagement in metacognitive activities are essential to technology integration in the classroom. However, the interplay between teachers’ TPACK ability and their metacognitive skills is still underexplored, especially in the context of developing technology‐infused lesson plans. This study examined how the interrelations among metacognitive activities and TPACK constructs affected preservice teachers’ technology integration in instructional design. Sixty‐four preservice teachers designed a lesson with nBrowser, a computer‐based learning environment (CBLE) that helps teachers incorporate technology into instruction by promoting self‐regulated learning (SRL). Drawing on the lesson plans, we extracted six types of metacognitive processes preservice teachers exhibited while solving the task and generated two distinct SRL profiles according to the identified latent profile of metacognitive patterns. The competent self‐regulated learners demonstrated more efforts in metacognitive monitoring activities than the less competent self‐regulated learners in regulating their task solving processes. When comparing TPACK comprehension and design performance between the two profiles, the competent self‐regulated learners outperformed the less competent self‐regulated learners on comprehension and design outcomes. This study provides deep insights into teachers’ self‐regulation in CBLEs and emphasizes the pivotal role of metacognition and SRL in teachers’ TPACK development. Practitioner Notes: What is already known about this topic Success in technology integration calls for teachers’ conceptual understanding of technological pedagogical content knowledge (TPACK). Teachers’ self‐regulated learning (SRL) ability mediates their TPACK development since metacognitive activities in the SRL process enable teachers to monitor and evaluate TPACK learning towards the sophisticated levels of TPACK. What this paper adds nBrowser, the computer‐based learning environment fosters teachers’ engagement in regulated TPACK development. Analysis of teachers’ lesson plans affords opportunities to identify teachers’ specific metacognitive processes in self‐regulated TPACK development. Latent profile analysis helps to understand the heterogeneity of teachers’ metacognitive processes and establish distinctive profiles regarding teachers’ self‐regulation. Teachers’ TPACK development differed significantly across distinctive SRL profiles. Implications for practice and/or policy Teacher educators should find ways to support teachers’ SRL ability in technology education. The identification of SRL profiles could contribute to designing metacognitive scaffolds in the specific domain of teachers’ technology education.