AI evidence extraction

Learner Engagement & Motivation in EFL Higher Education : The Pedagogical Role of Generative AI Tools in Educational Ecosystems

Abubaker Alhitty, Julie‐Ann Sime, Brett Bligh · Lancaster EPrints (Lancaster University) · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

Methodology & findings

Study design

Descriptive case study design involving 167 undergraduate learners with open-ended survey responses analyzed using reflexive thematic analysis

Sample

N = 167, 3 groups

Primary method

Reflexive thematic analysis of qualitative survey responses; informed by UTAUT-2 and Self-Determination Theory frameworks. No quantitative statistical tests are mentioned.

Main result

Findings indicate that students perceived notable improvements in areas such as "writing, vocabulary development, and grammatical awareness, reporting a shift from passive reception of corrective feedback to more autonomous, iterative interaction with GenAI tools." Additionally, "Learners described heightened intrinsic motivation and self-regulation, often engaging with AI tools beyond formal course requirements."

Reports effect sizes.

Research paradigm

Interpretivist/Qualitative

Author conclusions

The authors conclude that "the study contributes to technology-enhanced language learning by offering empirically grounded insights and actionable recommendations for the ethical, balanced, and pedagogically sound integration of GenAI into EFL curricula." They emphasize that the research develops "the Generative AI Engagement and Motivation (GAIEM) Framework that captures four interrelated dimensions, Enabling Environment, Mediated Engagement, Internalisation Mechanisms, and Learner Agency, illustrating how GenAI use is embedded within broader educational ecosystems."

Risk of bias

Single institutional context may introduce selection bias; Self-reported survey responses vulnerable to social desirability bias; Lack of control group for comparison; Potential attrition bias not mentioned in abstract; Selection bias: Single institutional context limits representativeness; Self-selection bias: Open-ended survey responses may attract motivated respondents; Lack of control group: No comparison condition to assess GenAI-specific effects; Attrition not addressed: No mention of response rates or attrition analysis; Potential social desirability bias: Students may overstate positive experiences with emerging technology; No blinding possible in qualitative design; Selection bias: single institution, non-randomized participants; Self-selection bias: participants choosing to engage with GenAI tools; Response bias: open-ended survey responses subject to social desirability bias

Limitations

The research is situated within a single institutional context, which limits generalizability
As stated by the authors: "While the research is situated within a single institutional context, it identifies avenues for future work, including longitudinal studies, cross-institutional comparisons, and investigations incorporating teacher perspectives."

Open questions raised

Longitudinal studies, cross-institutional comparisons, and investigations incorporating teacher perspectives
Longitudinal studies, cross-institutional comparisons, and investigations incorporating teacher perspectives. The authors note that "while the research is situated within a single institutional context, it identifies avenues for future work."
Longitudinal studies, cross-institutional comparisons, investigations incorporating teacher perspectives, and broader examination of GenAI adoption across diverse institutional contexts

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