Background: Social accountability represents the social contract between medicine and society, encouraging healthcare professionals (HCPs) to address social and health-related issues. The importance of integrating social accountability into curricula is widely recognised, but there is a lack of comprehensive mapping of the specific elements that should be included. Objectives: To identify the key elements of social accountability that should be integrated into undergraduate health sciences curricula to develop socially accountable HCPs. Method: The scoping review was conducted following the Joanna Briggs Institute Reviewers’ Manual 2015 for scoping reviews. A comprehensive search was employed using various keyword combinations and search strings, inclusive of published and grey literature from the past 15 years. Studies were systematically charted and analysed. Results: A rigorous screening process resulted in 47 studies being included in the review. Majority of the studies were qualitative, with the highest number of studies originating from Canada, South Africa, and the United States, as well as several multi-country studies. Equity emerged as the most frequently mentioned value, while cost-effectiveness was discussed the least. Conclusion: The scoping review demonstrates that embedding equity-driven approaches, community engagement, interprofessional collaboration and transformative learning in healthcare systems and tertiary institutions is vital. Addressing these priorities through undergraduate health sciences training can foster more inclusive, responsive and effective healthcare delivery, and improve health outcomes. Clinical implications: Integrating the identified elements of social accountability into undergraduate health sciences curricula may lead to improved patient outcomes, reduced health disparities, and more effective, patient-centred care.

Academic self-efficacy is a critical psychological construct influencing learners’ motivation, persistence, and achievement in foreign language learning; however, empirical evidence in secondary-level EFL contexts remains limited. This study investigated the effects of gamification on the academic self-efficacy of Grade 12 students learning English as a Foreign Language (EFL) in public secondary schools in Chonburi Province, Thailand. Employing a mixed-methods explanatory sequential design, the study first adopted a quasi-experimental pre-test–post-test approach, followed by focus group interviews to gain in-depth insights into students’ learning experiences. A total of 83 students participated, with 42 assigned to an experimental group receiving instruction integrated with Duolingo for Schools and 41 assigned to a control group receiving traditional instruction. Quantitative data were analyzed using paired-sample and independent-sample t-tests. The results revealed a statistically significant improvement in academic self-efficacy among students in the experimental group, with mean scores increasing from 2.85 (SD = 0.45) to 4.10 (SD = 0.35), while the control group obtained a lower post-test mean score of 3.40 (SD = 0.40) (p < .01). Qualitative findings corroborated the quantitative results, indicating that the gamified learning environment promoted engagement, motivation, and autonomous learning through features such as immediate feedback, progress tracking, and game-based challenges. The findings suggest that gamification, when systematically integrated into formal EFL instruction, can significantly enhance students’ academic self-efficacy. This study provides empirical support for the pedagogical value of gamified learning environments and offers practical implications for educators and policymakers seeking to strengthen learner engagement and psychological readiness in secondary-level EFL classrooms.

Effective communication, motivation, and self-efficacy are essential competencies for nursing students to deliver high-quality patient care. Educational strategies grounded in motivational theory, such as the ARCS model, may enhance these outcomes. This study aimed to examine the effects of an education program based on the ARCS motivational model on nursing students' communication skills, learning motivation, and academic self-efficacy. A mixed-method randomized controlled trial was conducted with sixty-four first-year nursing students randomly assigned to either an experimental group (n = 34), which received six hours of ARCS-based communication training, or a control group (n = 34), which received standard instruction. Quantitative data were collected pre- and post-intervention using validated instruments measuring communication skills, learning motivation, and academic self-efficacy. Qualitative data were obtained from reflective journals written by the experimental group and analyzed through inductive descriptive content analysis. The experimental group demonstrated significant improvements in ego-supportive language, active listening, and empathy (p < 0.05), whereas no significant change was observed in the I-language subscale. Only the experimental group showed significant increases in learning motivation and academic self-efficacy (p < 0.05), while the control group remained stable. Qualitative findings generated three overarching themes-Transformation in Communication, Motivation through ARCS, and Internalization and Development-which supported the quantitative results and illustrated enhanced emotional empathy, perceived relevance, and satisfaction. Education based on the ARCS motivational model significantly improved nursing students' communication skills, motivation, and self-efficacy. While affective and participatory competencies developed substantially, cognitive-reflective components may require extended or repeated interventions. The ARCS framework offers a robust pedagogical approach to fostering learner engagement and readiness in nursing education. Registered at ClinicalTrials.gov (NCT07004205 Registration date: 12.05.2025).

This study examined the effects of a mobile-mediated flipped classroom model informed by output-oriented instructional principles on English idiom learning and student engagement in Chinese higher education. A quasi-experimental design was implemented with 104 undergraduate English majors assigned to either a flipped instruction group ( n = 52) or a traditional lecture-based control group ( n = 52). Pre-class digital materials and collaborative in-class speaking tasks were delivered through WeChat to support idiomatic output practice. Idiomatic competence was measured using pre-/post-oral tests, while multidimensional engagement was assessed through the Higher Education Student Engagement Scale (HESES). Results showed that the flipped group achieved significantly higher idiomatic proficiency and stronger engagement than the control group. Post-test scores demonstrated a substantial achievement advantage for the flipped cohort, and engagement scores were higher across academic, cognitive, behavioral, and affective domains. The findings indicate that mobile-supported flipped instruction can enhance idiomatic learning by increasing opportunities for structured language output and interactive collaboration. The study contributes evidence for integrating mobile platforms into output-focused EFL pedagogy to support advanced linguistic development and learner engagement.

The digital transformation of education, propelled by technologies like AI and big data, has led to a proliferation of online learning platforms. However, abundant digital resources has arouse the challenges of low learning engagement and insufficient personalized learning. To address the challenges, this study proposes the construction and application of knowledge graph empowered POA (Production-Oriented Approach) teaching model to provide personalized learning for English listening course. By constructing knowledge graph empowered POA teaching model and and apply it into empirical study, it is to provide students with knowledge they need and optimize their personalized learning paths so as to ensure their learning engagement. Specifically, in this study, knowledge graph about English numbers in listening teaching is constructed, by implementing POA model embedded in UMOOCs and Chaoxing Xue Xitong, learning data are collected in pre-class, in-class, and post-class activities. Relying on the collection and analysis of dynamic data, students’ learning profiles are generated to reflect their learning status and ability levels. In the knowledge graph-empowered POA teaching model, students can personalize their learning according to their status by choosing the knowledge they need, thus improving learning engagement. This approach optimizes the allocation of teaching resources, drives personalized learning, and effectively improves teaching quality.

The integration of Artificial Intelligence (AI) in secondary education offers transformative potential but also presents significant challenges, particularly in developing-country contexts. This qualitative case study explored senior secondary school teachers’ attitudes, practices, and strategic needs for AI integration in Kicukiro District. Twelve purposively selected senior teachers from four secondary schools (two public and two private) participated in semi-structured interviews, and data were analyzed thematically. Findings revealed that teachers generally perceive AI as a powerful tool for enhancing lesson planning, learner engagement, and personalized instruction, while emphasizing that it should augment rather than replace human teaching. However, integration is constrained by limited infrastructure, inequitable access to digital devices, insufficient professional training, and ethical concerns, including over-reliance on technology and data privacy. Participants highlighted the importance of balanced pedagogical approaches that combine AI with human interaction. The study concludes that effective and ethical AI integration requires inclusive policies, sustained professional development, clear ethical frameworks, and context-sensitive implementation strategies. These findings provide actionable insights for policymakers, school leaders, and teacher education institutions to support responsible and equitable AI adoption in Rwandan secondary schools.

The integration of Information and Communication Technology (ICT) is increasingly recognized as a driver for educational inclusivity and improved learning outcomes. This study investigated basic ICT literacy as a tool for educational inclusion in under-resourced public secondary schools in Nagazi–Adavi Local Government Area, Kogi State, Nigeria. A descriptive survey design adopting a mixed-methods approach was used. Data were collected through structured questionnaires administered to 344 respondents comprising students, teachers, and non-teaching staff, alongside in-depth interviews with six senior school personnel. Quantitative data were analyzed using descriptive statistics and Chi-square tests at the 0.05 level of significance, while qualitative data were analyzed thematically. The findings revealed that ICT facilities were largely inadequate and that ICT use during classroom teaching was minimal. Although ICT was widely perceived to improve understanding and increase students’ interest in learning, no significant relationship was found between ICT availability and educational inclusion. Although, basic ICT literacy showed a significant relationship with students’ engagement in teaching and learning. In addition, challenges such as inadequate infrastructure, unreliable electricity supply, and limited training opportunities were found to significantly hinder effective ICT integration. The qualitative findings supported the survey results, highlighting infrastructural deficiencies alongside positive attitudes towards ICT. The study concludes that ICT has strong potential to enhance educational inclusion and learning engagement but remains constrained by systemic challenges. It recommends targeted government investment in ICT infrastructure, provision of alternative power sources, periodic ICT training for teachers, curriculum-based ICT integration, and strengthened multi-stakeholder partnerships.

As generative AI becomes increasingly integrated into higher education, understanding how students engage with these technologies is essential for responsible adoption. This study evaluates the Educational AI Hub, an AI-powered learning framework, implemented in undergraduate civil and environmental engineering courses at a large R1 public university. Using a mixed-methods design combining pre- and post-surveys, system usage logs, and qualitative analysis of students' AI interactions, the research examines perceptions of trust, ethics, usability, and learning outcomes. Findings show that students valued the AI assistant for its accessibility and comfort, with nearly half reporting greater ease using it than seeking help from instructors or teaching assistants. The tool was most helpful for completing homework and understanding concepts, though views on its instructional quality were mixed. Ethical uncertainty, particularly around institutional policy and academic integrity, emerged as a key barrier to full engagement. Overall, students regarded AI as a supplement rather than a replacement for human instruction. The study highlights the importance of usability, ethical transparency, and faculty guidance in promoting meaningful AI engagement. A total of 71 students participated across two courses, generating over 600 AI interactions and 100 survey responses that provided both quantitative and contextual insights into learning engagement.

The integration of pedagogy, educational technology, and assessment has been extensively discussed in English language learning research; however, instructional design frameworks that systematically align these three components to support meaningful English language learning remain limited. This study aimed to develop an integrated instructional design framework that bridges pedagogical strategies, technology integration, and assessment practices in the context of English language learning. A conceptual qualitative approach was employed through an integrative literature review and systematic document analysis. The data sources comprised reputable journal articles, academic books, systematic reviews, and international policy documents published between 2010 and 2025, which were analyzed using thematic analysis. The results identified five main themes, namely learner-centered pedagogy, authentic learning activities, the functions of interactive technology, formative and authentic assessment, and integrated instructional design. The findings indicate that meaningful English language learning consistently emerges when learning objectives, learning activities, technology use, and assessment methods are coherently integrated within a structured instructional design. This study provides a theoretical contribution by positioning meaningful learning as an integrated instructional system and a practical contribution in the form of a framework to support lesson planning and curriculum development. The findings underscore the importance of coherence in instructional design for enhancing communicative competence, learning engagement, and the sustainability of English language learning outcomes.

Mathematics, as a school subject, is consistently evaluated as one of the hardest, and Filipino students always rank the lowest in the world in mathematics proficiency examinations. This study looked into the situation of Instructional Delivery in General Mathematics for Senior High School students in one of the schools in Cebu. The study used a descriptive quantitative design, collecting data through document analysis and student surveys to evaluate teaching, content delivered, and student learning outcomes in a grounded manner. Based on the data, findings indicate that only 18% of the General Mathematics content standards were covered in instruction, a mere fraction exposing severe gaps in the implementation of the curriculum, and the resulting competencies. This also implies that current instructional materials and methods fail to meet the subject's cognitive and applied dimensions. This lack of sufficient material is the reason the Instructional Delivery, Content Standards, and Assignments Per Curriculum Published Guide need to be revised. Furthermore, for teachers to improve their instructional and pedagogical methods, curriculum integration of instruction, learning technologies, and student engagement in active learning to improve their understanding, professional development is critical. Creating a friendly and encouraging atmosphere where students feel engaged is necessary for maintaining motivation and self-assurance in mathematics. It is also important to have ongoing assessments in place, along with feedback, to measure progress and ascertain that instructional goals are achieved appropriately. Last but not least, enhancing mathematics instruction is vital in closing the learning gaps, developing students’ higher- order thinking and preparing them for the mathematics they need to apply in real life as well as in their further studies.

Artificial Intelligence (AI) is transforming mental health service delivery within public health systems, offering innovative tools for assessment, intervention, and policy-level decision-making. Children with special needs—including those with developmental, learning, and neurodiverse conditions—often face challenges in accessing timely mental health support, leading to delayed identification of emotional and behavioral difficulties and widening disparities in care. AI-driven mental health support provides opportunities to bridge these gaps by enabling early detection, personalized interventions, and continuous monitoring of psychological well-being. Aligned with the World Health Organization (WHO) framework, which emphasizes mental health promotion, prevention, early intervention, and community-based care, AI applications such as predictive analytics, digital screening tools, and adaptive therapeutic platforms can enhance the accuracy and efficiency of mental health assessments. These technologies also allow mental health professionals and educators to monitor progress, adjust interventions in real time, and provide scalable support in school and community settings. Similarly, the National Education Policy (NEP) 2020 highlights inclusive education, early identification of learning difficulties, and the integration of technology to provide personalized learning experiences. AI can support these goals by facilitating individualized educational and psychological plans, improving access to assistive technologies, and enhancing engagement for learners with special needs. While the potential benefits of AI are significant, ethical, legal, and policy challenges must be addressed. Issues such as data privacy, algorithmic bias, informed consent, equitable access, and over-reliance on technology require careful consideration. Human oversight, interdisciplinary collaboration, and evidence-based regulation are critical to ensuring that AI tools complement, rather than replace, traditional mental health services. This research work aims to explore AI-driven mental health support from a public health perspective, emphasizing the integration of technological innovation with ethical practice, inclusive education, and policy frameworks. By examining the intersection of AI, mental health, and special education, the discussion seeks to advance strategies for responsible, equitable, and effective mental health support for children with special needs, ensuring their holistic development and psychological well-being at a population level.

This study aims to explore the role of the social media platform TikTok in improving the English skills of Vietnamese university students. Data were collected through a cross-sectional survey completed by 105 student participants from universities across Vietnam, from the north to the south of the country. The study focused on students’ perceptions of TikTok's role in enhancing their listening, speaking, reading, and writing skills, as well as its influence on their motivation and confidence in using English for daily communication. The results showed that most students considered TikTok an effective platform for improving their English, with the greatest improvements reported in speaking and listening skills. In addition, students’ motivation and confidence in using English were significantly increased after using TikTok to practice. Findings suggest that social media platforms such as TikTok can support formal English language education and enhance learner engagement outside traditional classrooms.

Family stability is increasingly understood as a dynamic outcome shaped by cumulative life experiences rather than static structural conditions. This study examines the contribution of women’s learning trajectories to family stability, conceptualizing learning as a continuous and adaptive process unfolding across the life course. Using a quantitative explanatory design, survey data were collected from 312 adult women actively involved in family functioning. Women’s learning trajectories and family stability were measured as composite variables, and data were analyzed using linear regression and one-way analysis of variance. The results indicate that women’s learning trajectories significantly predict family stability (β = 0.61, p &lt; 0.001), explaining 37% of the variance in the outcome. ANOVA results further reveal significant differences in family stability across low, moderate, and high learning trajectory groups, with the highest stability observed among women with sustained learning engagement over time. These findings provide empirical support for life-course perspectives on learning, demonstrating that cumulative learning experiences among women play a central role in sustaining family stability. The study contributes to research on gender, education, and family by offering a trajectory-oriented quantitative approach with implications for lifelong learning and family-centered policy interventions.

With the in-depth development of English language teaching reform, the effectiveness of Communicative Language Teaching Method (CLT) and its mechanism of action in Chinese colleges and universities need to be studied in depth. Based on social cognitive theory and social constructivism, this study constructed a theoretical model of “CLT classroom participation - classroom atmosphere - self-efficacy in second language acquisition - commitment to second language learning,” and through a systematic survey of 656 college students from 7 colleges and universities across the country, the study reveals the innovative path of CLT in the Chinese educational context. The study found that: (1) CLT classroom participation positively affects students’ engagement in second language learning; (2) classroom atmosphere mediates the influence of CLT classroom participation on students’ engagement in second language learning; (3) self-efficacy for second language acquisition mediates the influence of CLT classroom participation on students’ engagement in second language learning; and (4) classroom atmosphere and self-efficacy form a chained mediation path. This finding breaks through overcomes the limitations of traditional CLT research that examines instructional variables in isolation. It is the first time that social cognitive theory and social constructivism have been systematically integrated, and a model of CLT mechanism applicable to Chinese educational culture has been proposed. The dual-path intervention strategy of “creating a supportive atmosphere and enhancing students’ sense of efficacy” is proposed for teacher training based on the empirical results, and specific suggestions are provided for the localization of CLT in Chinese classrooms, which is an important guiding value for improving the quality of English language teaching in China and promoting the innovation of teaching methods.

Artificial Intelligence (AI) is increasingly deployed in English-as-a-foreign-language (EFL) education, offering adaptive feedback, automated evaluation, and personalized learning pathways. However, existing research overwhelmingly emphasizes AI adoption and performance benefits, while largely overlooking what happens when AI systems fail to meet learner expectations and how learners recover from such failures. As a result, the cognitive–affective processes through which expectation violations translate into disengagement—or are mitigated through recovery—remain under-theorized and empirically unexplored. Addressing this gap, this study proposes and tests a cognitive–affective recovery model of learner engagement in AI-supported EFL contexts. Drawing on Expectation Violation Theory (EVT), Cognitive Appraisal Theory (CAT), and Digital Divide/Resilience Theory, the model explains how expectation violations influence engagement and how cognitive reappraisal and trust recovery mediate this relationship, while digital grit conditions learners’ ability to persist following setbacks. A two-wave survey of 298 Chinese EFL learners from urban and rural settings, including both university students and private institute learners, was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that expectation violations significantly reduce learner engagement, but perceived AI adaptivity narrows the adaptation gap and activates recovery processes. Cognitive reappraisal and trust recovery emerged as key mediating mechanisms, while digital grit moderated critical pathways by sustaining engagement under adverse conditions. By shifting the focus from AI success narratives to failure-and-recovery dynamics, this study advances theory on AI–learner interaction and offers practical guidance for designing resilient, trust-sensitive, and equity-oriented AI systems in language education.

Clinical reasoning is a core competency in medical practice; however, deficits in this domain among residents are often difficult to identify and remediate because of its cognitive complexity and the absence of standardized assessment approaches. This scoping review aimed to map and analyze existing evidence on strategies to remediate clinical reasoning skill deficits in underperforming medical residents. Using the Arksey and O’Malley framework as refined by Levac and his colleagues, and reported in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we systematically searched PubMed, Scopus, MEDLINE, Web of Science, SpringerLink, ProQuest, and EBSCOhost for studies published between 2000 and 2024. Definitions of clinical reasoning, underperformance, and remediation were adopted from prior literature. Twenty studies met the inclusion criteria, comprising original research and literature reviews in multiple medical specialties. Methods for identifying clinical reasoning deficits included written, oral, and performance-based assessments, as well as routine workplace-based evaluations. Remediation strategies ranged from structured institutional programs to individualized, case-specific interventions, with coaching, deliberate practice, guided reflection, and structured thinking frameworks frequently employed. Two studies reported positive outcomes following completion of remediation for clinical reasoning deficits. Key enablers included psychological safety, learner engagement, and accessible faculty support, whereas barriers included learner resistance, inadequate baseline knowledge, faculty skill limitations, and institutional resource constraints. Effective remediation requires early identification, comprehensive diagnostic assessment, and tailored, coaching-based interventions supported by institutional commitment. Nonetheless, substantial variability in definitions, remediation protocols, and evaluation methods highlights the need for greater standardization and further research across diverse contexts to inform evidence-based frameworks for clinical reasoning remediation.

Artificial intelligence-driven educational systems have largely prioritised cognitive adaptation, often neglecting the critical role of learners' emotional states in shaping engagement and learning outcomes. To address this limitation, this study proposes a multimodal, emotion-aware deep learning framework designed to integrate emotional intelligence into intelligent learning environments. The framework jointly analyses facial expressions, speech characteristics, and textual responses to infer learners' emotional states and models the interdependencies among these modalities through a graph-based fusion mechanism. The proposed approach is evaluated using benchmark emotion datasets, namely AffectNet and IEMOCAP, to assess its capability to recognise emotional patterns and support adaptive feedback during learning interactions. Experimental results demonstrate that incorporating emotional awareness leads to substantial improvements in learner engagement, emotional regulation, and task persistence when compared with conventional cognition-focused systems. The framework achieves consistently high emotion recognition performance, particularly for positive and neutral affective states, and shows robust generalisation across different emotion categories. User study outcomes further suggest that learners perceive the system as more supportive and responsive due to its emotional adaptability. In addition to performance evaluation, the study discusses key ethical considerations associated with emotion-aware educational technologies, including data privacy, informed consent, and responsible deployment. Overall, the findings underscore the potential of multimodal emotional intelligence to advance the development of more empathetic, adaptive, and effective artificial intelligence-based educational systems.

The present study explains how inclusive STEAM education founded on the Sustainable Development Goals (SDGs) may help to reduce equity gaps and support sustainable learning. A qualitative research design was adopted through the adoption of an interpretive approach. The researchers employed the semi-structured interview of educators and school leaders who worked with the curriculum based on STEAM and implemented it in their schools. The data was analyzed through thematic analysis to establish important patterns within the context of inclusion, pedagogy, and sustainability. The results indicate that inclusive STEAM education improves equal participation and engagement of learners alongside relevance through collaboration, creativity, and solving real-life problems. The relevance to SDG-oriented themes enhanced sustainability awareness and skills in the learners. The barriers to successful implementation were noted to be the issues of limited resources and teacher preparation as well as the institutional backup support. Study fulfill the gap in the literature by incorporating inclusion, STEAM education, and the SDGs into a single pedagogical model to provide insights into the work of educators, policymakers, and organisations interested in promoting equitable and sustainable education.

General Background: Histology is a fundamental component of preclinical medical education that requires mastery of microscopic interpretation and integration of structure–function relationships. Specific Background: First-year medical students frequently perceive histology as challenging due to high cognitive load, abstract visual content, reliance on memorization, and limited motivation associated with conventional lecture-based teaching. Knowledge Gap: Although gamification has been increasingly applied in medical education, there remains limited synthesis focusing specifically on its application, outcomes, and limitations within undergraduate histology learning, particularly regarding standardized evaluation and long-term sustainability. Aims: This narrative literature review aims to examine the relevance of gamification in basic histology education, identify commonly used gamification tools, and summarize reported educational outcomes across undergraduate medical curricula. Results: The reviewed literature indicates that gamification strategies, including points, badges, leaderboards, interactive quizzes, and digital challenges, are associated with improved learner engagement, motivation, knowledge retention, self-confidence, and active classroom participation across cognitive, affective, and behavioral domains. Novelty: This review consolidates evidence on gamification specifically within histology education while simultaneously highlighting methodological limitations, novelty effects, competitive risks, and design heterogeneity reported in existing studies. Implications: Carefully designed, theory-driven, and context-adapted gamification frameworks, integrated with digital microscopy and evaluated through longitudinal research, may support more interactive and meaningful histology learning experiences for early-stage medical students. Highlights: Interactive game-based strategies are consistently associated with higher learner participation and motivation in microscopic science learning. Reported outcomes span cognitive, affective, and behavioral domains, including retention, satisfaction, and active learning behaviors. Design variability and limited long-term evaluation remain key challenges in current implementations. Keywords : Gamification, Histology Education, Medical Students, Engagement, Preclinical Learning

Abstract This study introduces SmartPD, an AI-assisted, micro-learning-based professional development (PD) model designed to enhance teachers' digital competence in line with the DigCompEdu framework. Existing PD initiatives are often generic, insufficiently contextualised, and lack mechanisms for personalisation, scalability, and iterative feedback, limiting their long-term impact. To address these gaps, this research adopts a Design-Based Research (DBR) methodology, following four iterative phases: (i) contextual exploration of teachers' digital competence using the DigCompEdu Check-In tool, (ii) design and development of SmartPD modules grounded in micro-learning and AI based feedback, (iii) short-term implementation with 32 in-service teachers, and (iv) evaluation and refinement based on analytics and participant feedback. The intervention integrated H5P for interactive content, Flipgrid for peer reflections, and ChatGPT for real-time formative feedback, with learner engagement tracked through xAPI dashboards. Data were collected through pre- and post-assessments, reflective journals, and platform analytics. Results indicated statistically significant improvements in digital competence across all five DigCompEdu domains, with the most substantial gains in "Facilitating Learners' Digital Competence" and "Teaching and Learning." Teachers also reported high levels of satisfaction and confidence, while engagement data confirmed strong usability and high completion rates. Despite limitations related to sample size and duration, SmartPD demonstrates the potential of combining DBR with AI-enabled micro-learning to provide scalable, context-sensitive, and evidence-based teacher professional development, aligned with SDG 4: Quality Education.