The Potential and Implications of Generative AI on HCI Education
Project Overview
The document examines the incorporation of generative AI (GAI) in Human-Computer Interaction (HCI) education, specifically through a 10-week undergraduate module at Newcastle University. It details how students utilized GAI tools to create personas, scenarios, and functional requirements, leading to valuable pedagogical insights. Key findings indicate that GAI serves as a reflective tool that deepens students' understanding of design concepts while fostering creativity and engagement. However, the study also underscores the necessity for educators to clarify common misconceptions regarding GAI's capabilities and limitations. By critically evaluating these aspects, the paper advocates for innovative applications of GAI in educational practices, balancing its potential benefits with an awareness of its constraints. Overall, the integration of GAI in this educational context demonstrates significant opportunities for enhancing learning experiences and outcomes in HCI.
Key Applications
Generative AI tools (e.g., ChatGPT, Bard, Claude) for creating personas, scenarios, and functional requirements.
Context: Undergraduate HCI and Interaction Design module for computing science students.
Implementation: A coursework-based module where students worked in groups to use GAI tools to generate design resources, followed by a survey to gather feedback on their experiences.
Outcomes: Students reported increased engagement, creativity, and understanding of design principles, as well as improved ability to articulate their knowledge of HCI concepts.
Challenges: Some students relied too heavily on GAI for evaluation of their designs, leading to concerns about misconceptions regarding GAI's capabilities and limitations.
Implementation Barriers
Perceptual
Students may attribute too much reliability to GAI for evaluating their designs, leading to overconfidence in the AI's feedback.
Proposed Solutions: Educators should explicitly teach the limitations of GAI models and promote critical thinking about AI outputs.
Technological
Students may struggle to understand the nuances of using GAI effectively, especially in generating high-quality outputs. This includes difficulties in prompt design and the importance of contextual details.
Proposed Solutions: Provide structured guidance on prompt design and emphasize the importance of contextual details to improve GAI output quality.
Project Team
Ahmed Kharrufa
Researcher
Ian G Johnson
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Ahmed Kharrufa, Ian G Johnson
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18