Beginner-level learners with limited vocabulary are underrepresented in AI-assisted language learning research

The gap

Evidence profile

Sourced from the conclusions and future work and stated research gap of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• AI-Generated Errors as a Learning Tool: Improving Programming Education Through Error Correction (2026) · The Journal of Educators Online · doi

  Future research could address these limitations by incorporating multiple program- ming languages, expanding the range of covered topics, and exploring alternative student-AI inter- action models. Additionally, the study did not examine the effects of allow- ing students to actively generate erroneous code using the chatbot, an area that warrants further investigation. Furthermore, the possible implications of inte- grating AI chatbots into programming education are not limited to their use in individual learning settings.

  generalconclusions
  Keywords: future address limitations incorporating multiple program ming languages expanding range covered topics exploring alternative student
• Enhancing programming education with emotion-aware chatbot interfaces: a Wizard-of-Oz study among Arabic-speaking university students (2026) · International Journal of Educational Technology in Higher Education · doi

  This study examined the role of emotion-aware AI chatbots in programming education through a controlled classroom experiment involving Arabic-speaking computer sci- ence students. Four instructional setups were compared: text-based chatbot interaction, voice-based chatbot interaction, voice-based interaction with an animated avatar, and a real teacher simulating chatbot responses. By combining subjective reports, objective engagement measures, affective analysis, and expert code evaluation, the study provided a comprehensive assessment of both learning performance and emotional experience during a real Java programming task. Nawahdah et al. International Journal of Educational Technology in Higher Education (2026) 23:22 Page 21 of 25 The findings indicate that voice-based interaction, particularly when augmented with an emotionally expressive avatar, offers clear advantages over text-based and real- teacher-simulated setups. Voice and Avatar conditions were associated with higher and more sustained engagement, more stable positive emotional states, and stronger performance in code readability and maintainability (with no significant differences in accuracy). While task completion was highest in the Voice and Real Teacher setups, the Avatar condition demonstrated the most consistent balance between emotional regu- lation, engagement continuity, and structural code quality. These results suggest that combining vocal interaction with emotionally expressive avatars can approximate key aspects of supportive teacher presence, such as empathy, adaptive pacing, and motiva- tional feedback, while remaining scalable for larger classroom contexts. Beyond empirical outcomes, this work contributes one of the first controlled, class- room-based studies to directly compare text, voice, real teacher, and avatar-based instructional modalities within a single programming task for Arabic-speaking learn- ers. In doing so, it moves beyond conceptual design recommendations and provides experimentally validated evidence that multimodal, emotionally adaptive interaction can enhance engagement, emotional stability, and code quality. The findings also highlight the importance of cultural and linguistic alignment: Arabic-language voice interaction, combined with emotion-aware feedback, created a more natural and psychologically comfortable learning environment for participants. The implications are relevant for both educators and system designers. For educa- tors, the results suggest that voice- and avatar-based chatbots can support engagement and emotional regulation in cognitively demanding subjects such as programming. For developers, the study underscores the value of multimodal interaction, affective respon- siveness, and culturally sensitive design in educational AI systems

  generalfuture work
  Keywords: based interaction voice avatar real teacher engagement emotional programming code arabic setups text chatbot task
• From Anxiety to Autonomy: A Case Study of Beginning EFL Students Using AI Conversational Apps for Self-Regulated Practice in Omani Higher Education (2026) · Journal of Language Teaching and Research · doi

  Beginner-level learners with limited vocabulary are underrepresented in AI-assisted language learning research. Most studies focus on advanced users capable of sustained chatbot interaction, leaving a gap in understanding the effectiveness of AI conversational tools for beginner-level learners. There is a need to explore the impact of AI corrective feedback and self-regulated engagement on learners' affective filters.

  generalstated research gapevidence 5/5
  Keywords: beginner-level learners limited vocabulary underrepresented ai-assisted language learning

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