AI Literacy and the Role of Generative AI in Higher Education

Key Concepts

• Objective: Exploring the impact of Generative AI (GenAI) tools on AI literacy among higher education students.

  • Study Design: Convergent mixed-methods case study in a U.S. university.

• Context: 37 graduate students enrolled in three 8-week courses on advanced digital technologies in education.

• Types of GenAI Tools:

  • GenAI reviewer for essay assessments.
  • GenAI image generation for reflections on learning experiences.

• Findings:

  • Increased comfort with GenAI tools and enhanced ability to understand their strengths and limitations.
  • Students learned about responsible AI applications in education.

Historical Context of AI in Education

• Evolution of AI: Developments from the 1950s to present (e.g., LLM chatbots like ChatGPT).
• Categories of AI in Education:
  1. Learning for AI.
  2. Learning about AI.
  3. Learning with AI.

AI Literacy

• Definition: Understanding AI technologies and their responsible/critical application.

• Skills Involved:

  • Critical understanding of AI functionalities.
  • Ability to communicate and cooperate with AI systems.

• Current Research Gaps: Few empirical studies on practical applications of GenAI in educational contexts.

• Importance of AI Literacy: Essential for academic and employment success in an AI-dependant society.

Previous Studies on AI Literacy Development

• Laupichler et al.: Need for further research on practical aspects of AI literacy in education.
• Kong et al.: Evaluated an AI literacy course showing significant improvements in students’ understanding of AI across various demographics.
• Fathahillah et al.: Addressed AI literacy in web programming courses during COVID-19, highlighting understanding of AI implications and data security.

Pedagogical Approach

• Cyber-social Teaching Method: Combination of AI tools with human intelligence to enrich learning experiences.
• Cognitive Prostheses: AI enhances human cognitive tasks, implying a partnership between humans and AI.

Study Implementation

• Educational Context: Online graduate courses combining live and asynchronous learning.
• Emphasis on Collaboration: Use of peer reviews alongside AI-generated feedback to enhance learning.
• AI Review Tool: Developed to provide feedback based on course-specific rubrics, enhanced through participant interaction.

Data Collection and Analysis

• Methods: Pre- and post-course surveys + thematic analyses of reflections.

• Participants: Graduate students with varied demographic backgrounds, experience with AI tools, primarily education professionals.

• Expectations: Data analyses aimed to determine changes in perceived AI literacy.

Results and Findings on AI Literacy Development

• Post-course Survey Insights: Significant growth in AI familiarity and confidence levels in using AI tools (Post-course means: familiarity = 3.22 vs. pre-course = 2.62; confidence = 3.27 vs. pre-course = 2.41).

• Participants’ Reflections: Engaged in iterative processes of prompt crafting for image generation; perceived AI tools as collaborative partners enhancing their work.

  • Identified strengths and weaknesses in AI-generated vs. peer feedback.

Pedagogical Recommendations

1. Instructional Strategies:

   • Incorporate various multimodal AI tools.
   • Create assignments that foster an exploratory mindset.

2. Reflective Learning:

   • Encourage metacognitive reflections and peer knowledge sharing.

3. Ethical and Critical Engagement:

   • Reinforce skills to critically evaluate AI tools and their implications.
   • Integrate discussions on ethical considerations regarding AI usage.

Limitations of the Study

• Small sample size with reliance on self-reported data might cause bias.
• Short duration of the intervention limits generalization.

Conclusion

• AI Tools in Education: Potential to significantly enhance student learning and understanding of AI.
• Future Directions: Emphasize a holistic understanding of AI that integrates ethical considerations into curricula.