Perceptions of Generative AI for Literary Text Interpretation Among Pre-Service TESL Teachers in Malaysia

Generative Artificial Intelligence (GenAI) tools are becoming increasingly common in how pre-service English as a Second Language (ESL) teachers engage with literary texts. However, this usage creates a pedagogical tension with Reader-Response Theory (RRT) which advocates for authentic, learner-driven meaning-making rather than passive consumption. Drawing on the Technology Acceptance Model (TAM) alongside RRT, this study examined how pre-service TESL teachers perceive the usefulness and ease of use of GenAI in literary interpretation. A sequential explanatory mixed-methods design was employed at a Malaysian public university, involving a questionnaire with 125 TESL undergraduates and semi-structured interviews with eight purposefully selected participants. Survey findings revealed highly positive perceptions of GenAI’s usefulness (73.6%–87.2% agreement), particularly for summarising texts into clear main ideas. Perceived ease of use was also positive but more cautious (64.8%–79.2%), with the lowest agreement regarding the idea that GenAI requires little mental effort. Thematic analysis of the interview data clarified these patterns. Participants valued GenAI as a bridge to decode complex language and cultural references, and to organise ideas, explore alternative perspectives, and validate interpretations. However, a profound tension emerged regarding authorship. While GenAI enhanced output depth, learners feared cognitive dependency and a loss of interpretive originality. Furthermore, while technically accessible, GenAI's effective application required iterative prompt engineering and introduced a substantial new cognitive burden of verifying hallucinated or superficial outputs. The study concludes that GenAI is most useful when treated as an interpretive scaffold rather than an absolute authority on literary meaning. Consequently, TESL teacher education should promote critical AI literacy so that GenAI supports, rather than replaces, learners’ own meaning-making.

Generative Artificial Intelligence, Reader-Response Theory, Technology Acceptance Model, Literary Interpretation, Pre-service TESL teachers

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