Artificial Intelligence in English Language Assessment: Opportunities and Ethical Concerns – A Balochistan‑Focused Analysis

Zualfiqar Ali; Dr. Muhammad Fahim Baloch; Pervez Ahmed

doi:10.5281/zenodo.19762632

Authors

Zualfiqar Ali PhD Scholar, Gomal University, Dera Ismail Khan, KPK, Pakistan
Dr. Muhammad Fahim Baloch Associate Professor, Department of Media Studies, University of Balochistan, Quetta
Pervez Ahmed Assistant professor, Pakistan study center, University of Balochistan, Quetta

DOI:

https://doi.org/10.5281/zenodo.19762632

Keywords:

artificial intelligence, English language assessment, automated essay scoring, algorithmic bias, accent discrimination, Balochi, Pashto, Brahvi, Saraiki, Punjabi, data privacy, teacher oversight, offline AI, edge computing, internet shutdowns,Balochistan.

Abstract

Automated Essay Scoring (AES) softwares and AI-based Speaking assessment software are being utilized in classroom and in testing Centres worldwide during integrating AI in learning and teaching. This paper outlines opportunities and ethical issues of AI integration in English language assessment, emphasizing on algorithmic bias, data privacy and oversighting role of English teacher. Based on recent empirical studies, such as those that show that there are racial and linguistic prejudices in the grading of GPT-4o essays that were so serious. Indeed, still there is a continuous discrimination due to diverse accent in automated speech recognition systems. The review has also revealed that the current AI assessment technologies, although they seem to be efficient, consistent, and scalable are fundamentally incapable of evaluating language impartially, amongst heterogeneous learners. Especially, this article pays special attention on the learners whose native languages are Balochi, Pashto, Saraiki and Punjabi limiting the scope to Pakistan generally and Balochistan particularly. This article also considers and addresses the critical infrastructural issues of Balochistan (sporadic electricity, poor internet connection, internet blackouts caused by security) and suggests a tangible, offline-first, edge-computing implementation paradigm, which can allow complex AI evaluation even in the most isolated and security-impacted districts of Balochistan. Then this article recommends a human-focused, hybrid assessment model where AI is used as a supportive tool and not as a substitute to teacher judgment that is based on transparency standards and periodic bias audits and transparent policies regarding data governance. The conclusion provides context-specific practical recommendations to educators, institutions, and policymakers who wish to use AI assessment tools in a responsible manner in the context of Balochistan during English language teaching.

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