Achieving Sustainable Development Goal 4 (Quality Education) requires innovative and ethically grounded integration of emerging technologies in higher education. This study developed and empirically validated an ethics-by-design AI-in-Instructional-Design Framework integrating Microsoft Copilot and Google Gemini into university course development workflows. Using a mixed-methods developmental research design guided by the ADDIE model, the framework was piloted with 12 educators and 180 students across blended and online courses. Quantitative analyses revealed statistically significant improvements in instructional quality (+10.44), UDL compliance (+15.21), workflow usability (+20.78 SUS), and reductions in faculty preparation time (-3.13 hours/week). Student engagement, AI literacy, integrity compliance, and accessibility adoption also increased significantly (p<.001). Thematic analysis of qualitative feedback confirmed enhancements in workflow efficiency, alignment, inclusivity, and ethical governance while emphasizing the importance of human oversight. Findings indicate that structured, ethics-governed AI integration enhances instructional quality, equity, and responsible innovation in higher education, thereby advancing SDG 4.

Early integration of Artificial Intelligence (AI) in education has introduced new learning paradigms including prompt engineering-a vital skill for effectively interacting with AI systems. Nevertheless, teaching prompt engineering in higher education continues to be challenging because it is very technical and doesn't have engaging ways to teach it. In this paper, we propose to develop a gamified web-based teaching and learning platform offering prompt engineering through interactive modules and personalized learning environments. The platform implements AI-driven feedback mechanisms to analyze students' prompts for learning and offers students real-time guidance. It also includes gamification elements such as points, badges, and leaderboards, to maintain students' interest and motivation. It combines personalization and gamification, aiming that students will improve their understanding of prompt engineering concepts, applying them with AI technology in their own environments. These mixed methods of research also take usability testing and measurements of learner performance and engagement surveys. This research enhances the emerging literature on AI in education by providing an innovative approach for the development of skills in the digital age.

The integration of Artificial Intelligence (AI) in education has transformed learning environments, enabling more efficient and adaptive instruction. This study examines the impact of Microsoft Copilot, an AI-powered assistive technology, on student performance and engagement in blended learning. A mixed-methods approach was employed, combining pre-test/post-test analysis, LMS data, surveys, interviews, and focus groups. Results indicate that Copilot improves instructional efficiency, enhances engagement, and supports real-time assessment, although concerns about overreliance persist. The findings contribute to understanding AI's role in modern education and support the development of smarter digital learning systems.

In order to meet the need for scalability in academic advising in higher education settings while ensuring compliance, fairness, and privacy, we propose a governance-ready multi-agent reinforcement learning framework that aggregates the advantages of four highly specialized agents, namely, the Plan Validator, Risk Detector, Resource Recommender, and Conversational Advisor, which function collaboratively with a policy learner and under the supervision of a human. In a quasi-experimental mixed-methods study involving n = 345 participants from different departments, the system yielded a substantial improvement over the baseline in terms of efficiency and quality, where the turnaround time in academic advising was reduced by ≈60% or from 48.6 to 19.3 hours, while compliance was boosted by +11.3% and risk detection recall was enhanced by +0.12 or from 0.67 to 0.79. The stability of the learning process was satisfactory, with a normalized cumulative reward of +0.35 and around 1,200 episodes to converge. For the governance control, the effectiveness of the proofs was satisfactory, as the fairness gap reduced from 7.8% to 3.2%, while the privacy incidents remained zero even with continuous audits. In terms of evaluation, a mixed methods evaluation design was used, which included the performance records of the RL, ANOVA subgroup, and trust judgments to establish validity. From the findings, the MARL solution is technically viable, ethical, and feasible as an advisory mechanism in HEIs. Suggestions are made for future directions to investigate the solution's scalability and dynamic reward shaping for various policies.

The proposed research aims to develop a hybrid learning analytics model that incorporates clickstream and sentiment mining techniques to accurately predict student retention in Learning Management Systems (LMS) in Manila-based HEIs. This proposed research aims to improve the limitations of single prediction modeling. The proposed research utilized a quantitative explanatory research design and was applied to undergraduate students taking general education courses. The proposed research utilized stratified sampling to select a total of 345 students. Clickstream and text-based interaction data were analyzed to obtain sequence and sentiment-based metrics to train the hybrid prediction model using Logistic Regression and XGBoost. The engagement tool was also able to reach Cronbach's alpha = 0.91, which is a good measure of internal consistency. This shows that the click-only model had an AUC of 0.78, but the hybrid model was enhanced to 0.84, with the top predictors being attempts at quizzes per week and negative sentiment ratio. This shows the importance of multimodal analytics in the improvement of early warning systems and proactive interventions. The implication of this can be seen in the administration of the institution and the academic support, which is a basis for evidence-based retention and fairness-based predictive models in Philippine Higher Education.