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.

Gamification has come up as a powerful tool in increasing learning engagement and motivation in online education. As a proposal in this article, the designing and developing of AI-powered game modules in GameMaker for STEM education with adaptive learning capabilities in a gamified setup is ideal. The proposed model can regulate AI in a controlled manner to personalize learning experiences based on game gameplay with adaptive difficulty levels, immediate feedback, and analysis of performance data of learners. Based on developing a game using GameMaker, this research utilizes AI analytical capabilities for achieving learning outcomes in STEM education by aligning game targets with learning in STEM education. A quasi-experimental design was utilized to assign 60 purposively sampled STEM students into experimental and control groups in equal measures. The data gathering methods used in this study include pre and post-tests, in-game engagement metrics and usability surveys based on SUS and TAM. Salient findings show that the AI-enhanced module produced significantly greater learning gains (M=19.80) than the non-adaptive module (M =11.10). Improvements within both groups were significant (p < 0.001), while between-group comparison favored the experimental group (p=0.002). Engagement metrics further revealed that the AI-adaptive group exhibited longer time-on-task, higher level-completion rates, and more active use of hints-indicating sustained engagement and effective utilization of adaptive support.

The proliferation of artificial intelligence (AI) in schools like higher ed has largely been centered around learning through text-based sources, often lacking visual components, even though these are essential in many subjects (STEM): science, technology, engineering, and mathematics. In this paper, we propose an AI-focused learning framework that integrates Microsoft Copilot Vision to accelerate visual perception and understanding in university settings. The system will interpret pedagogical diagrams, charts, and handwritten notes to develop them into organized textual explanations and interactive learning materials. It aims at making text with natural language generation accessible and engaging as well as encouraging cognitive development in learners with image recognition. Experimental observation on open education datasets and real classroom resources show to better enhance learner understanding and lower the cognitive load than classical static visual aids. This study provides a foundation for intelligent, multimodal learning that closes the gap between visual and linguistic perception, leading to personalized and inclusive education in higher learning institutions.

This paper presents a design framework for integrating AI-generated visuals into blended and hybrid learning environments, based on the Cognitive Theory of Multimedia Learning and Dual Coding Theory. It emphasizes responsible AI practices following UNESCO guidelines. The framework was piloted in blended courses, showing notable benefits such as instructor time savings, enhanced student engagement, reduced cognitive load, and better comprehension. The study offers a validated and ethical method for employing text-to-image technologies in blended learning.