FOUR DIMENSIONS OF AUTHENTICITY IN AI LANGUAGE ASSESSMENT

Аннотация

This article presents a detailed framework of four essential dimensions of authenticity in AI-enhanced language assessment: contextual, interactional, consequential, and representational. For each dimension, we analyze current AI capabilities, identify limitations, and suggest pathways for development. This framework provides TESOL practitioners with concrete criteria for evaluating AI assessment tools and offers developers clear guidelines for creating more authentic assessment systems. Keywords: language assessment, artificial intelligence, authenticity dimensions, TESOL, assessment design 1. Introduction As artificial intelligence technologies increasingly permeate language education, there is a pressing need for frameworks that can guide their implementation in ways that support rather than undermine communicative language teaching principles. This paper presents a four-dimensional framework of authenticity specifically designed for AI-enhanced language assessment. 2. Contextual Authenticity Contextual authenticity concerns the extent to which assessment tasks reflect real-world language use contexts. This dimension addresses: 2.1 Situational Relevance Assessment tasks should represent situations that learners are likely to encounter in target language environments. Current AI systems often rely on generic, decontextualized prompts that fail to mirror the specific contexts in which learners will use language. Next-generation systems should incorporate situationally embedded tasks that reflect authentic communicative contexts. 2.2 Purpose-Driven Communication Authentic language use is motivated by genuine communicative purposes. AI assessment should evaluate language within purposeful tasks rather than as isolated performance. While current systems typically assess language as product rather than process, emerging technologies could better simulate authentic communicative purposes. 2.3 Cultural Appropriateness https://scientific-jl.com 2 7 2 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 should account for cultural norms and expectations relevant to the target language community. AI systems trained on limited cultural data may fail to recognize culturally appropriate language use or may impose culture-specific expectations inappropriately. Culturally responsive AI requires diverse training data and cultural awareness in assessment design. 2.4 Multimodal Integration Real-world communication integrates multiple modes including verbal, visual, and contextual cues. AI assessment that focuses solely on linguistic features without considering multimodal aspects of communication compromises contextual authenticity. Multimodal AI systems represent a promising direction for enhancement. 3. Interactional Authenticity Interactional authenticity addresses the dynamic, reciprocal nature of authentic communication: 3.1 Contingent Responses Authentic communication involves unpredictable exchanges where responses are contingent on preceding turns. Current AI dialogue systems typically follow predetermined paths with limited ability to handle unexpected responses. LLMs show potential for more contingent interaction but still lack true understanding of conversational dynamics. 3.2 Turn-Taking Management Assessment should consider how learners navigate conversational structures including initiation, response, and follow-up moves. AI assessment rarely evaluates turn-taking skills, focusing instead on isolated responses to prompts. Dialogue-based systems with enhanced interactional capabilities could address this limitation. 3.3 Repair Strategies Authentic communication involves identifying and resolving communication breakdowns. AI assessment typically provides one-way feedback rather than engaging in collaborative repair. Systems that can simulate authentic repair sequences would enhance interactional authenticity. 3.4 Co-construction of Meaning Meaning emerges through collaborative interaction rather than individual production alone. Current AI systems typically evaluate individual production against predetermined criteria rather than considering how meaning is negotiated between participants. Truly interactive AI would need to participate in and evaluate co- construction processes. 4. Consequential Authenticity Consequential authenticity concerns the impact of assessment on teaching, learning, and stakeholder perceptions: 4.1 Washback Effects https://scientific-jl.com 2 7 3 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 should promote positive teaching and learning practices aligned with communicative language teaching principles. If AI assessment focuses primarily on machine-readable features of language, it may encourage teaching practices that prioritize those features over communicative competence. 4.2 Stakeholder Acceptance Assessment methods should be perceived as valid and meaningful by teach