Objective:

To explore the use of AI in identifying threshold concepts in analytical chemistry education that may be overlooked due to expert familiarity.

Key Findings:

• AI can surface candidate threshold concepts that experts may overlook due to familiarity.
• Thematic aggregation of concepts helps identify recurring patterns and conceptual tensions.
• Experts found value in AI-generated insights, prompting reevaluation of their assumptions.

Interpretation:

AI serves as a calibration tool, revealing biases in instructors' perceptions of conceptual difficulty and enhancing the understanding of student struggles.

Limitations:

• The framework may risk becoming overly theoretical if not grounded in practical application.
• Reliance on AI outputs requires careful expert evaluation to ensure relevance and utility.

Conclusion:

AI can enhance analytical chemistry education by identifying critical threshold concepts, prompting instructors to reconsider curriculum design and student support.