USING AI FOR AUTOMATED LANGUAGE ASSESSMENT: PROS AND CONS
Keywords:
Keywords: AI-powered language assessment, automated language evaluation, AI in education, natural language processing (NLP), machine learning in assessment, automated writing evaluation, AI speech assessment, fairness in AI assessment, AI bias in language testing, human-AI collaboration in education.Abstract
Abstract: Artificial Intelligence (AI) has revolutionized language assessment
by providing automated evaluation tools for various linguistic skills, including
speaking, writing, grammar, and vocabulary. AI-powered assessment systems offer
several advantages, such as efficiency, scalability, and consistency in evaluation. They
can provide instant feedback, reducing the workload for human examiners and enabling
large-scale testing. Additionally, AI-driven assessments can help learners track their
progress and personalize learning experiences. However, AI-based language
assessment also presents challenges. One major concern is the potential lack of
accuracy and fairness, as AI may struggle with nuanced language elements such as
creativity, cultural context, and emotional expression. Bias in training data can lead to
unfair evaluations, particularly for non-native speakers. Moreover, AI systems may not
fully capture the complexities of human communication, such as tone, intent, and
idiomatic expressions. Ethical concerns regarding data privacy and the role of human
educators in language learning also need to be addressed. This paper explores the
advantages and disadvantages of AI-driven language assessment, considering its
impact on learners, educators, and language testing methodologies. While AI offers
promising advancements in language assessment, a balanced approach combining AI
with human expertise may be necessary to ensure fairness, accuracy, and effectiveness.
References
Reference:
1. Attali, Y. & Burstein, J. (2006). “Automated Essay Scoring with e-rater® V.2.”
2. Shermis, M. D. & Burstein, J. (Eds.). (2003). Automated Essay Scoring: A Cross-
disciplinary Perspective. Lawrence Erlbaum Associates.
3. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence
Unleashed: An Argument for AI in Education. Pearson Education.
4. Jurafsky, D. & Martin, J. H. (2008). Speech and Language Processing: An
Introduction to Natural Language Processing, Computational Linguistics, and
Speech Recognition. Prentice Hall.
5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
6. Burstein, J., Chodorow, M., & Leacock, C. (2003). “CriterionSM Online Essay
Evaluation: An Overview.” In Shermis, M. D. & Burstein, J. (Eds.), Automated
Essay Scoring: A Cross-disciplinary Perspective. Lawrence Erlbaum Associates.
7. Hansen, J. H. L. & Jurafsky, D. (2001). “Intonation in Spontaneous Speech:
Analysis and Modeling.” In Proceedings of the International Conference on Spoken
Language Processing (ICSLP).
8. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases
Inequality and Threatens Democracy. Crown.
9. Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce
Racism. NYU Press.
10. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence
Unleashed: An Argument for AI in Education. Pearson Education.
