Human-in-the-Loop Explanation Evaluation: Empirical Frameworks for Assessing the Real-World Impact of Explainable NLP in Clinical and Educational Practice

Mohammadi et al. (2025) and Milad & Whiba (2024) note that most explainable NLP research lacks robust, domain-specific evaluation of how explanations perform in real-world settings. Do explanations improve clinician trust or diagnostic accuracy? Do they help students learn better or teachers grade more fairly? This idea proposes developing and testing empirical protocols—such as randomized controlled trials, user studies, or simulation-based audits—to quantify the impact of explanations on user outcomes (e.g., decision quality, learning gains, trust, and satisfaction). The novelty here lies in moving beyond technical metrics (e.g., faithfulness, fidelity) to real-world, human-centered impact metrics in education and healthcare. Such studies would fill a critical gap, providing much-needed evidence on the practical utility and risks of explainable NLP in sensitive, high-stakes domains.

References:

1. Explainability in Practice: A Survey of Explainable NLP Across Various Domains. Hadi Mohammadi, Ayoub Bagheri, Anastasia Giahanou, D. Oberski (2025). arXiv.org.
2. Exploring Explainable Artificial Intelligence Technologies: Approaches, Challenges, and Applications. Akram Milad, Mohamed Whiba (2024). International Science and Technology Journal.