Presented at the IAFOR International Conference on Education in Hawaii, this study examined how prompt engineering influences the quality and educational value of AI-generated feedback. The research highlights the importance of pedagogically informed prompt design in enhancing feedback effectiveness and supporting meaningful learning experiences in higher education.
This case study examined how technology-enhanced teaching practices, combined with systematic use of learner data, can support more equitable, responsive, and reflective learning experiences in higher education. The study explored the use of digital tools, learning analytics, formative feedback, and reflective teaching practices to inform instructional decision-making and improve student engagement.
Presented at the International Conference on e-Learning & Innovative Pedagogies in Greece, this study introduced a framework that links learning data, reflective practice, and educational technology to support equitable and evidence-informed teaching. The framework provides a practical model for transforming classroom evidence into meaningful instructional improvement.
This presentation explored how generative artificial intelligence may reshape feedback practices in higher education. The study examined the emerging possibilities of AI-mediated feedback, focusing on personalization, scalability, learner engagement, and the evolving role of educators within increasingly technology-enhanced learning environments.
This study investigated the potential of ChatGPT3 to provide meaningful and personalized feedback in higher education. Combining a systematic review of the literature with empirical analysis, the research explored the effectiveness, opportunities, and challenges of AI-supported feedback and contributed early evidence to the growing field of artificial intelligence in education.
This early investigation explored the potential of ChatGPT3 to provide personalized feedback within educational contexts. Through a combination of systematic literature review, empirical feedback analysis, and theoretical examination, the study evaluated the pedagogical value of AI-generated feedback and its implications for teaching and learning.
