second-language-assessment
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- 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
The study compared an empathetic spoken dialogue system (E-SDS) with a neutral one (N-SDS) to assess interactional competence in L2 oral assessment. Results showed that the E-SDS elicited higher frequencies of interactional competence features and was perceived by learners as a competent, trustworthy, and emotionally supportive interlocutor, though 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.
- PaperERIC — Assessment & second language1 Jan 2026
Extrinsic Drivers of Scientific Impact in Second Language Assessment: A Bibliometric Path Analysis
Sai Zhang, Vahid Aryadoust
A bibliometric path analysis of 447 language assessment papers (2018-2022) found that seven extrinsic factors—self-citations, article age, journal CiteScore, number of countries, number of authors, open access, and research topic—significantly influence citation counts, accounting for 57.75% of variance. These findings highlight the role of non-quality factors in citation-based research evaluation.
Original abstract
This study examined how multiple factors, independent of research quality, influence the scientific impact of language assessment articles. A total of 447 papers published between 2018 and 2022 were investigated using a path analysis approach to identify both direct and indirect ways in which extrinsic factors affect citation counts. A conceptual path model was constructed based on existing literature and assessed using a comprehensive bibliometric dataset. The final model showed an excellent fit to the data (X[superscript 2] = 65.6, df = 52, p = 0.097; standardized root mean square residual [SRMR] = 0.034, root mean square error of approximation [RMSEA] = 0.024, comparative fit index [CFI] = 0.980, Tucker-Lewis index [TLI] = 0.970) and accounted for 57.75% of the variance in citation counts (R[superscript 2] = 0.5775, p < 0.001). Among nine examined factors, seven demonstrated significant direct or indirect effects on citation counts: number of self-citations, article age, journal CiteScore, number of countries, number of authors, open access, and research topic. Based on these findings, the study deepens our understanding of citation practices and offers broader implications for citation-based research evaluation in language assessment.