This study is based on the development of a design focused on underlining what artificial intelligence can achieve to bring value to learning quality especially when implementing active methodologies, such as project-based learning (PBL). This study analyzed the perceptions of AI-integrated PBL versus non-AI-integrated PBL among teachers in primary, secondary, and higher education. Conducted with a sample of teachers (n = 300), this study investigated perceived effectiveness, the AI-powered personalization of learning, and motivation. A Student’s t-test, as well as normality, homogeneity of variance, and Cohen’s d tests, revealed that AI-enhanced PBL is rated significantly higher than regular PBL without AI, with a Cohen’s d effect size of 1.30, signifying a large impact. These findings underpin the development of an optimized AI-driven PBL model, particularly within both the prototype production and evaluation phases, providing greater autonomy, responsive feedback, and adaptive personalization, all towards advancing a more effective AI-supported pedagogical model of teaching.

Several major themes emerged across the contributions included in this Research Topic. The published studies collectively explored the pedagogical, psychological, technological, and ethical implications of artificial intelligence integration in educational environments. Across the Topic, particular attention was given to generative AI and ChatGPT in higher education, adaptive and personalized learning systems, metacognition and self-regulated learning, student motivation and emotional dimensions, teacher preparedness, AI literacy, and the responsible implementation of intelligent technologies in educational practice. One of the most prominent themes identified throughout the Research Topic was the growing integration of generative artificial intelligence and conversational systems into higher education environments. Several studies examined the pedagogical implications of ChatGPT and other AI-driven tools in academic writing, collaborative learning, student motivation, and educational interaction.Collectively, these contributions reflect the rapid transition of generative AI from an experimental technological resource to an increasingly normalized component of educational practice. Studies such as those by Chukwuere et al., Sánchez-Rivas et al., Agostini et al., and Afzaal et al. explored The importance of personalization also appeared in studies focused on multimedia integration, digital literacy, and AI-enhanced educational resources. Alsaffar examined the impact of AI-enhanced multimedia quality, ease of integration, and usage frequency on learning effectiveness, identifying technological self-efficacy and student engagement as important mediating factors. Similarly, Xie et al. investigated students' acceptance of AI eBooks in China, reporting significant differences associated with digital literacy levels and perceived usefulness. Within this broader discussion, open learner models and metacognitive approaches emerged as particularly relevant pedagogical developments. Robles Mucho et al. emphasized the capacity of pedagogically enriched open learner models to support self-regulated learning, metacognitive engagement, and learner agency. Rather than functioning solely as analytical systems, these models were conceptualized as reflective and adaptive educational tools capable of supporting more transparent, student-centered, and participatory learning processes.Beyond personalization and adaptive instruction, several contributions in this Research Topic emphasized the psychological, metacognitive, and emotional dimensions of AI-enhanced learning.Collectively, these studies suggest that artificial intelligence is increasingly influencing not only how students access information, but also how they regulate learning processes, construct knowledge, manage emotions, and engage with educational experiences.One of the most conceptually distinctive contributions was presented by Tomisu et al., who proposed the "Cognitive Mirror" framework for AI-powered metacognition and self-regulated learning. In contrast to approaches that position AI as an omniscient provider of answers, the authors argued for a pedagogical shift toward human-centered AI systems capable of supporting reflection, explanation quality, and active knowledge construction. Their framework conceptualized AI as a metacognitive partner rather than a purely automated instructional tool, emphasizing the importance of preserving human agency, critical thinking, and reflective learning processes. Related perspectives also emerged in the review conducted by Robles Mucho et al., which highlighted the pedagogical potential of open learner models for promoting self-regulated learning, metacognitive engagement, and learner autonomy. Together, these contributions reinforce the idea that AI-enhanced educational systems may support learning more effectively when they are designed not simply to optimize performance, but also to strengthen reflective and participatory learning processes. Several empirical studies additionally explored the relationship between AI-supported learning environments, motivation, anxiety, and emotional engagement. Hamd et al. examined the use of artificial intelligence for assessing academic exam anxiety, perceived stress, and achievement motivation among university students, reporting significant interactions between perceived stress and achievement motivation before examinations.Similarly, Huang and Kakham investigated AI conversational agents in foreign language learning, finding improvements in oral proficiency and reductions in situational speaking anxiety in specific communicative tasks.Motivational dimensions were also explored in studies grounded in technology acceptance frameworks. Afzaal et al. analyzed the relationship between intrinsic motivation and ChatGPT acceptance in higher education, identifying perceived usefulness, ease of use, and intrinsic motivation as important predictors of behavioral intention toward AI adoption. Likewise, Susanto et al. examined AI adoption through a TAM-SDT perspective, highlighting both the motivational benefits and the potential risks associated with problematic or excessive technological use. Creativity and collaborative interaction also emerged as recurring concerns across multiple contributions. Ramos-Núñez et al. reported that while AI-supported story-based learning environments may enhance engagement and brainstorming processes, students who relied less heavily on AI perceived themselves as more creative and socially connected. Similar tensions were identified by Agostini et al., whose findings suggested that AI-supported collaborative lesson design environments may increase efficiency while simultaneously reducing opportunities for authentic peer-to-peer dialogue and idea generation. Taken together, these studies illustrate the increasingly complex relationship between AI-enhanced educational environments, learner autonomy, creativity, emotional...