The Socratic Challenger: a structured GenAI-assisted workflow for undergraduate research inquiry

A mixed-methods study assessed an eight-step Generative AI (GenAI)-assisted workflow designed for undergraduate research question development in an ecology course. This workflow integrated AI-supported exploration with iterative human processes including literature verification, revision, and feedback. The research aimed to clarify the role of GenAI in early-stage academic inquiry, utilizing both quantitative ratings of workflow steps and qualitative student reflections from 45 participants.

Why it matters

The integration of Generative AI into research methodologies represents a significant technological shift that can enhance educational and operational efficiencies. Understanding how structured GenAI workflows support complex tasks like research inquiry is crucial for organizations looking to leverage AI responsibly and effectively. This research provides insights into potential frameworks for adopting AI in knowledge-intensive domains.

Key insights

• The study evaluated an eight-step GenAI-assisted workflow for developing research questions in an undergraduate ecology course.
• The workflow incorporated alternating stages of GenAI-supported exploration and human-led activities like literature verification, revision, and feedback.
• Data was collected from 45 consenting undergraduate students, using ordinal ratings for perceived contribution of workflow steps and written reflections.
• The research focused on understanding the utility of GenAI in the early phases of academic inquiry, specifically research question formulation.

Source

Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1913451