Varied Approaches
AI and the Art of Collaboration: When Machines Meet Human Creativity
Paper Presentation in a Themed Session
Shalom Yabilsu
As artificial intelligence (AI) becomes increasingly ingrained in creative industries, its role in education presents challenges and opportunities. While AI is often framed as a threat to originality and academic integrity, it also holds the potential to be a powerful tool for creative collaboration. This paper examines how AI can serve as an active participant in the creative process through a case study in a non-graphic design classroom. By integrating AI-assisted design into an academic project, this study explores how students interact with generative AI tools, navigate ethical concerns, and critically engage with emerging technologies. Beyond the technical exploration, the project became a platform for broader discussions about authorship, intellectual property, and the ethical implications of using AI-generated content. Key questions emerged, such as whether selecting and modifying AI-generated images constitutes original work or whether presenting AI-generated outputs without attribution is a form of plagiarism. These discussions highlighted the necessity of developing AI literacy in educational settings, ensuring students understand both the creative potential and ethical responsibilities of AI-assisted work. This case study highlights the importance of actively engaging with emerging technologies rather than shying away from them. Rather than positioning AI as a disruptive force that diminishes creativity, this approach reimagines AI as a collaborative partner—one that, when used thoughtfully, enhances creative expression and expands the boundaries of academic and creative exploration.
Enhancing Learning Through Synergy: AI and Peer Feedback in Higher Education
Paper Presentation in a Themed Session
Christopher Hughes,
Akash Saini
This study investigates students' perceptions of feedback from Generative Artificial Intelligence (GenAI) and human peers in cyber-social learning environments. The research aims to understand the effectiveness of different feedback mechanisms in dynamic educational spaces where digital platforms and social interactions converge. This study contributes to the ongoing discourse on integrating digital tools in education, with implications for educational design and practice in the evolving cyber-social learning landscape. This research employs a mixed-methods approach using structured surveys containing quantitative rating scales and qualitative open-ended items. The study was conducted at an American university in Spring 2024, involving 86 participants from various degree programs within the College of Education. Findings reveal nuanced student perspectives on the strengths and weaknesses of both feedback types, with AI reviews perceived as higher quality and more useful, while actionability was comparable between AI and human reviews. The study's limitations include its context-specific nature and the use of a particular learning platform. This research provides empirical evidence to inform the development of effective feedback strategies in cyber-social learning environments, suggesting that a combination of GenAI and human-generated feedback could offer the best of multiple qualitatively different types of intelligence.
A Framework for Optimising Generative AI Interactions in Undergraduate Education
Paper Presentation in a Themed Session
William Ko Wai Tang
Prompt engineering involves the strategic formulation of clear and structured instructions to optimise interactions with generative AI. As AI technologies become increasingly integrated into educational practices, it is essential for educators to understand how to effectively guide these systems to enhance teaching and learning outcomes for undergraduate students. This study proposes a comprehensive theoretical framework that integrates established models, focusing on equipping educators with the necessary skills to assess and improve their prompt engineering practices. By examining the principles underlying these frameworks, the proposed model emphasises key competencies such as clarity, specificity, and iterative refinement. Through the development of this framework, the study seeks to provide insights into how prompt engineering can be systematically integrated into undergraduate education curricula.
