Integrating an educational chatbot in an undergraduate-level linguistics course: Cognitive load and student engagement

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dc.contributor.authorUysal, Derya
dc.date.accessioned2026-01-24T12:31:19Z
dc.date.available2026-01-24T12:31:19Z
dc.date.issued2026
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractArtificial intelligence in education increasingly leverages chatbots to provide on-demand support across diverse academic contexts. This study examined a curriculum-aligned chatbot in Structure of English, a first-year ELT course, focusing on student engagement and endline cognitive load through a Cognitive Load Theory lens. The design was an embedded single-case study with three units of analysis: behavioral engagement, affective engagement, and endline cognitive load. Participants were first-year ELT majors at a public university. Instruments included interaction logs, per-interaction satisfaction ratings (1-5), the 10-item Leppink Cognitive Load Scale (alpha = 0.849), and two open-ended questions on effectiveness, limitations, and improvement. Results showed that students submitted 6010 prompts ranging from 1 to 332 words (similar to 1-2236 characters), indicating both quick checks and multi-sentence inquiries. Weekly chatbot use tracked the assessment calendar: after a quiet start, it stabilized (approximate to 250-500 queries/week) before the midterm, declined post-midterm, and surged sharply before the final (approximate to 2500), indicating that exam timing strongly shaped behavioral engagement. Mean satisfaction was 4.28/5. Perceived usefulness emphasized independent learning (anytime/anywhere clarification), syllabus-aligned explanations, and better usability than the coursebook. Limitations included typo sensitivity, coverage gaps, and occasional irrelevant or repetitive replies. Endline cognitive load scores were IL = 6.01 (high), EL = 3.64 (low to moderate), and GL = 5.89 (medium). In conclusion, a course-aligned, outside-class chatbot was associated with frequent, varied engagement and positive perceptions, while yielding a CL profile typical of dense, abstract content-high IL, low to moderate EL, and medium GL.
dc.identifier.doi10.1016/j.tsc.2025.102099
dc.identifier.issn1871-1871
dc.identifier.issn1878-0423
dc.identifier.scopus2-s2.0-105024611452
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.tsc.2025.102099
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5805
dc.identifier.volume60
dc.identifier.wosWOS:001642014000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofThinking Skills and Creativity
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectChatbot
dc.subjectCognitive load theory
dc.subjectLinguistics course
dc.subjectLearning engagement
dc.subjectPre-service EFL teachers
dc.titleIntegrating an educational chatbot in an undergraduate-level linguistics course: Cognitive load and student engagement
dc.typeArticle

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