[Paper Review] The EAP-AIAS: Adapting the AI Assessment Scale for English for Academic Purposes

Jasper Roe, Mike Perkins+1 more|arXiv (Cornell University)|Aug 2, 2024

Online Learning and Analytics4 citations

TL;DR

This paper introduces the EAP-AIAS, a five-level framework adapting the AI Assessment Scale for English for Academic Purposes (EAP) contexts, enabling structured integration of Generative AI in EAP assessment while preserving academic integrity and supporting language development. The framework categorizes AI use from 'No AI' to 'Full AI' across writing, presentations, and research tasks, offering educators a pedagogically sound tool for ethical AI use in language education.

ABSTRACT

The rapid advancement of Generative Artificial Intelligence (GenAI) presents both opportunities and challenges for English for Academic Purposes (EAP) instruction. This paper proposes an adaptation of the AI Assessment Scale (AIAS) specifically tailored for EAP contexts, termed the EAP-AIAS. This framework aims to provide a structured approach for integrating GenAI tools into EAP assessment practices while maintaining academic integrity and supporting language development. The EAP-AIAS consists of five levels, ranging from "No AI" to "Full AI", each delineating appropriate GenAI usage in EAP tasks. We discuss the rationale behind this adaptation, considering the unique needs of language learners and the dual focus of EAP on language proficiency and academic acculturation. This paper explores potential applications of the EAP-AIAS across various EAP assessment types, including writing tasks, presentations, and research projects. By offering a flexible framework, the EAP-AIAS seeks to empower EAP practitioners seeking to deal with the complexities of GenAI integration in education and prepare students for an AI-enhanced academic and professional future. This adaptation represents a step towards addressing the pressing need for ethical and pedagogically sound AI integration in language education.

Motivation & Objective

• To address the growing challenge of integrating Generative AI into EAP instruction without compromising academic integrity.
• To adapt the existing AI Assessment Scale (AIAS) for the specific needs of EAP learners, who require both language proficiency and academic acculturation.
• To provide EAP educators with a structured, flexible assessment framework that supports pedagogical decision-making in AI-integrated tasks.
• To explore practical applications of the framework across diverse EAP assessment types, including writing, presentations, and research projects.
• To promote ethical and pedagogically sound AI integration in language education, preparing students for an AI-augmented academic and professional future.

Proposed method

• Adapting the original AI Assessment Scale (AIAS) into a context-specific version, the EAP-AIAS, tailored for EAP learning environments.
• Defining five distinct levels of AI use, ranging from 'No AI' to 'Full AI', with clear descriptors for each level to guide assessment and instruction.
• Applying the EAP-AIAS to various EAP assessment types, including academic writing, oral presentations, and research projects, to ensure broad applicability.
• Framing AI use within the dual goals of EAP: developing language proficiency and fostering academic acculturation.
• Providing educators with a decision-making tool that balances innovation with academic integrity in AI-assisted learning.
• Embedding the framework in pedagogical practice through alignment with EAP learning outcomes and assessment criteria.

Experimental results

Research questions

• RQ1 How can the AI Assessment Scale be meaningfully adapted for use in English for Academic Purposes (EAP) contexts?
• RQ2 What are the key considerations for integrating Generative AI into EAP assessment while maintaining academic integrity?
• RQ3 How can EAP educators apply a structured framework to evaluate and guide student use of AI in writing, presentations, and research tasks?
• RQ4 In what ways does the EAP-AIAS support both language development and academic acculturation in EAP learners?
• RQ5 How can the EAP-AIAS be flexibly applied across diverse EAP assessment formats to ensure pedagogical relevance and ethical use?

Key findings

• The EAP-AIAS provides a five-level framework that clearly differentiates acceptable AI use in EAP tasks, from no AI to full AI integration.
• The framework supports both language development and academic acculturation, addressing the dual focus of EAP education.
• The EAP-AIAS enables educators to make consistent, transparent, and ethically defensible judgments about student work involving AI.
• The framework is applicable across multiple EAP assessment types, including writing, presentations, and research projects, enhancing its pedagogical versatility.
• The EAP-AIAS offers a scalable and adaptable solution for institutions navigating the challenges of AI integration in language education.
• The framework positions educators as facilitators of responsible AI use, preparing students for future academic and professional environments shaped by AI.