IMPLEMENTING AUTHENTIC AI ASSESSMENT IN TESOL: CHALLENGES AND RESEARCH DIRECTIONS

Аннотация

This article addresses the practical challenges of implementing authentic AI- enhanced language assessment in TESOL contexts. Drawing on a four-dimensional framework of authenticity, we identify key implementation barriers at technological, pedagogical, and institutional levels. We propose a research agenda to address these challenges and offer practical guidelines for TESOL practitioners navigating the integration of AI assessment tools. The article concludes with recommendations for interdisciplinary collaboration between language educators, AI developers, and assessment researchers. Keywords: AI implementation, language assessment, TESOL, research agenda, educational technology 1. Introduction Artificial intelligence technologies offer promising possibilities for language assessment, but their successful implementation requires addressing significant challenges related to authenticity. This article examines implementation challenges through the lens of a four-dimensional authenticity framework (contextual, interactional, consequential, and representational) and proposes research directions to address these challenges. 2. Current Implementation Challenges 2.1 Technological Challenges Computational Resources: Truly authentic AI assessment may require substantial computing power not available in all educational contexts. Resource disparities may create inequitable access to high-quality assessment technologies. Technical Integration: Implementing AI systems within existing educational technology infrastructure presents challenges. Many language programs use learning management systems with limited AI integration capabilities. Data Requirements: High-quality AI assessment requires extensive training data. Smaller language programs may lack sufficient data for customization or validation. Algorithm Transparency: The “black box” nature of some AI systems complicates validation against authenticity criteria. Educators may be unable to determine how assessment decisions are made. 2.2 Pedagogical Challenges https://scientific-jl.com 2 6 6 65-son_1-to’plam_Iyul-2025 ISSN:3030- 3 6 1 3 T A D Q I Q O T L A R jahon ilmiy – metodik jurnali Assessment Literacy: Many language educators lack sufficient understanding of AI capabilities and limitations to implement these tools effectively. Balancing Assessment Types: Determining appropriate roles for AI versus human assessment remains challenging, particularly for complex language skills. Feedback Integration: Incorporating AI feedback into broader pedagogical approaches requires careful design to avoid overemphasis on machine-detectable features. Learner Resistance: Some learners may resist AI assessment due to concerns about validity, fairness, or preference for human evaluation. 2.3 Institutional Challenges Policy Development: Many institutions lack policies governing AI assessment use, raising questions about validity, accessibility, and academic integrity. Staff Development: Professional development related to AI assessment implementation is often inadequate. Cost-Benefit Analysis: Institutions struggle to evaluate return on investment for AI assessment technologies, particularly regarding authentic assessment outcomes. Ethical Considerations: Privacy concerns, data ownership, and potential bias in AI systems raise significant ethical questions that institutions must address. 3. Integration Matrix: Current Status The following matrix evaluates current implementation status across educational contexts: Educ Contextu Interac Consequ Represent ational al Authenticity tional ential ational Context Authenticity Authenticity Authenticity Highe Moderate Low - Variable Low - r Education - some limited - depends on limited contextualized dialogue implementation accommodation tasks capabilities of diversity Privat Low - Low - Variable Low - e Language standardized primarily - commercial standard language Schools assessments one-way pressures focus feedback K-12 Low - Low - Concerni Low - Settings often limited ng - potential normative decontextualize interaction negative approaches d washback https://scientific-jl.com 2 6 7 65-son_1-to’plam_Iyul-2025 ISSN:3030- 3 6 1 3 T A D Q I Q O T L A R jahon ilmiy – metodik jurnali Self- Moderate Low - Variable Low - Directed - some scripted - depends on mainstream Learning personalization interaction learner attitudes language models This matrix highlights significant gaps in current implementation, particularly regarding interactional and representational authenticity. 4. Practical Implementation Strategies 4.1 Short-Term Strategies Hybrid Assessment Approaches: Combine AI assessment with human evaluation, leveraging each for appropriate aspects of language performance. Contextual Scaffolding: Provide rich contextual information around AI assessment tasks to enhance contextual authenticity. Feedback Mediation: Train educators to help learners interpret and apply AI feedback within broader communicative contexts. Transparency Practices: C