interactional-competence
Filtering by topic interactional-competence(3)Clear all filters
- PaperLanguage Testing8 Jul 2026
“I Feel Like Talking With a Friend”: Exploring the Potential of Empathetic Spoken Dialogue Systems in Assessing Interactional Competence in L2 Oral Assessment
Lianzhen He, Ruixue Liang, Yang Zhao
This study compared an empathetic spoken dialogue system (E-SDS) with a neutral system (N-SDS) for L2 oral assessment, finding that the E-SDS elicited higher frequencies of interactional competence features. Learners perceived the E-SDS as a competent, trustworthy, and emotionally supportive interlocutor, though concerns about system design and technical limitations were noted.
Original abstract
As an important area of exploration within spoken dialogue systems (SDSs), empathetic spoken dialogue systems (E-SDSs) can provide emotional support for interlocutors, and this feature has the potential to be embedded in language teaching, learning, and assessment. However, the potential of E-SDSs in second language (L2) oral assessment remains underexplored, particularly regarding their ability to elicit interactional competence (IC) and learners’ perceptions of the two systems. To address these gaps, this study compares learners’ interaction with a neutral spoken dialogue system (N-SDS) with limited empathetic capability as a reference condition and an E-SDS, with an aim to explore its potential for L2 oral assessment. Twenty-five L2 learners completed two tasks (E-SDS and N-SDS). Their oral performances between the two tasks were examined in terms of IC features, and their perceptions of the E-SDS were also investigated through semi-structured interviews. Results indicated the E-SDS tends to elicit higher frequencies of certain IC features. Learners generally perceived the E-SDS as a competent, trustworthy, and emotionally supportive interlocutor, but certain concerns were also expressed concerning system design and technical limitations.
- PaperLanguage Testing8 Jul 2026
Assessing Interactional Competence Through Generative AI: Comparing Large Language Models as AI Interlocutors in the Paired Oral Discussion Test
Inyoung Na
This study compared GPT-4o and Claude 3.5 Sonnet as AI interlocutors in paired oral discussion tests for assessing interactional competence. The results showed that Claude outperformed GPT-4o in eliciting interactional competence features and was perceived as more authentic by test takers, highlighting the need for construct-driven evaluation criteria when selecting LLMs for language assessment.
Original abstract
Interactional competence (IC) is essential for oral communication assessment, yet human partner variability can introduce construct-irrelevant variance in paired speaking tests. As an alternative to a test with a human interlocutor, this study describes the development of a large language model (LLM)-driven Spoken Dialogue System and compares GPT-4o to Claude 3.5 Sonnet to inform model selection for IC assessment. Twelve international students completed paired discussion tasks with both LLMs in counterbalanced order. System performance was evaluated through breakdown activation consistency, stance maintenance, and persona adherence. Test-taker performances were analyzed using interactional discourse analysis to identify IC features across three dimensions: topic management, interactional management, and interactive listening. Semi-structured interviews explored test takers’ perceptions of the AI partners. Results showed Claude outperformed GPT-4o in eliciting IC features, successfully activating communication breakdown strategies and maintaining oppositional stance, thereby creating more opportunities for test takers to demonstrate key IC abilities. Test takers perceived Claude as more authentic and natural, while GPT was perceived as more artificial. These findings demonstrate that different LLMs create distinct interactional conditions affecting both IC elicitation and test-taker perceptions. The findings highlight the need for construct-driven evaluation criteria when selecting LLMs for language-assessment contexts.
- PaperRELC Journal11 May 2026
Listening beyond comprehension: Towards a taxonomy of second language interactive listening skills
Daniel MK Lam, Christine CM Goh
This paper argues that the construct of listening in second language education should include interactive listening skills for two-way communication. It reviews existing taxonomies, finds interactive listening underrepresented, and proposes a new taxonomy based on conversation analysis and interactional competence. The taxonomy aims to help teachers systematically develop learners' interactive listening abilities.
Original abstract
Listening in second language education has long been conceptualised and operationalised with an overwhelming focus on one-way, non-participatory listening. Although this is necessary for the development of cognitive dimensions of second language listening, it leaves a gap where learners are under-equipped for two-way, interactive listening, particularly the socio-interactional dimensions of ‘being listeners’. In this paper, we argue that the construct of listening for second language teaching and learning should include skills for interactive listening. We review prevailing taxonomies of second language listening skills that serve as the basis for reference standards, syllabuses and assessments, and highlight the under-representation and under-specification of interactive listening. We then draw on insights from conversation analytic research on spoken interactions and second language interactional competence, to shed light on the nature of listening and listener actions in interactions. On that basis, we propose a taxonomy of interactive listening skills that provides a framework for teachers to help learners develop these skills systematically and explicitly. Finally, we discuss the significance of interactive listening in language teaching research and practice, and outline an agenda for future research.