Automated Essay Scoring and Language Certification: Assessing Generalizability, Agreement and Validity for French
arXiv:2606.02009v1 Announce Type: new Abstract: In Automated Essay Scoring (AES), benchmarking practices have fostered minimalist evaluation practices, in contrast with the broader-view recommendations of evaluation frameworks, such as the argument-based validation framework (ABV), which argued in favor of a multidimensional assessment of systems, especially in the context of high-stakes language tests. In this paper, we introduce an enhanced and more practical version of the ABV framework, incorporating fairness analysis, correlations with linguistic features, prediction error evaluation, and
The proliferation of AI systems capable of natural language processing makes automated assessment increasingly viable, prompting a need for robust validation frameworks.
This development highlights the ongoing effort to ensure fairness and validity in high-stakes AI applications, particularly in education and certification, which has significant societal implications.
The proposed enhanced validation framework allows for a more comprehensive and ethical integration of AI into critical assessment processes, moving beyond minimalist evaluation approaches.
- · AI ethics and auditing firms
- · Educational technology providers leveraging AES
- · Language certification bodies
- · Students receiving fairer assessments
- · Providers of biased or unvalidated AES systems
- · Traditional manual essay scoring systems
More widespread adoption of AI in educational assessment, particularly for language proficiency.
Increased pressure on AI developers to incorporate fairness and robust validation into their models from inception to deployment.
Potential for new global standards for AI-driven assessment, influencing international education and professional credentialing.
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