From NLG Evaluation to Modern Student Assessment in the Era of ChatGPT: The Great Misalignment Problem and Pedagogical Multi-Factor Assessment (P-MFA)

Mika Hämäläinen, Kimmo Leiviskä Leiviskä


Abstract
This paper explores the growing epistemic parallel between NLG evaluation and grading of students in a Finnish University. We argue that both domains are experiencing a Great Misalignment Problem. As students increasingly use tools like ChatGPT to produce sophisticated outputs, traditional assessment methods that focus on final products rather than learning processes have lost their validity. To address this, we introduce the Pedagogical Multi-Factor Assessment (P-MFA) model, a process-based, multi-evidence framework inspired by the logic of multi-factor authentication.
Anthology ID:
2025.iwclul-1.1
Volume:
Proceedings of the 10th International Workshop on Computational Linguistics for Uralic Languages
Month:
December
Year:
2025
Address:
Joensuu, Finland
Editors:
Mika Hämäläinen, Michael Rießler, Eiaki V. Morooka, Lev Kharlashkin
Venues:
IWCLUL | WS
SIG:
SIGUR
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–5
Language:
URL:
https://aclanthology.org/2025.iwclul-1.1/
DOI:
Bibkey:
Cite (ACL):
Mika Hämäläinen and Kimmo Leiviskä Leiviskä. 2025. From NLG Evaluation to Modern Student Assessment in the Era of ChatGPT: The Great Misalignment Problem and Pedagogical Multi-Factor Assessment (P-MFA). In Proceedings of the 10th International Workshop on Computational Linguistics for Uralic Languages, pages 1–5, Joensuu, Finland. Association for Computational Linguistics.
Cite (Informal):
From NLG Evaluation to Modern Student Assessment in the Era of ChatGPT: The Great Misalignment Problem and Pedagogical Multi-Factor Assessment (P-MFA) (Hämäläinen & Leiviskä, IWCLUL 2025)
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PDF:
https://aclanthology.org/2025.iwclul-1.1.pdf