Key take-aways

Issues to Consider:

• AI-generated content is not always accurate or contextually appropriate, making critical human oversight essential.
• Students need training to evaluate AI outputs and distinguish useful insights from misleading ones.
• AI should be seen as a supplement – not a substitute – for engaging with academic literature. Students need prior knowledge of their topic to use AI effectively; otherwise, they may struggle to assess the quality of AI outputs.
• Some students expressed concerns about the stigma surrounding AI use, fearing their work might be perceived as less valuable. To address this, institutions should establish clear guidelines on AI usage in academic work to reduce uncertainty.

Recommendations:

• Courses should incorporate AI literacy components to teach students how to use AI responsibly. Instead of relying on AI to think for them, students should be encouraged to seek feedback and refine their own ideas.
• Based on the workshop experience, it is recommended that AI-based support be introduced after students have developed and presented preliminary research plans without AI. At this stage, students are better equipped to evaluate AI-generated content and apply it meaningfully to their own work.
• Universities should foster open discussions about the appropriate and ethical use of AI in academic settings.