Limited discussion of AI integration specifics, training

The gap

Evidence profile

Sourced from the open questions and limitations of the source papers, classified as methodology gap, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Development of the Russian as a foreign language teachers’ methodological competence in the artificial intelligence era (2026) · Russian Language Studies · doi

  There is a need to develop more effective methodologies for teaching prompt engineering and AI-based pronunciation assessment, as these were identified as particularly challenging topics.

  methodology gapopen questionsevidence 5/5
  Keywords: there need develop effective methodologies teaching prompt engineering based pronunciation assessment identified particularly challenging topics
• A Hybrid Play-Game-Based Learning Approach to Developing an Educational English Literacy App for Early Learners (2026) · International Journal of Computer Science and Mobile Computing · doi

  Limited discussion of AI integration specifics, training data, accuracy metrics for pronunciation assessment, or potential biases in the AI pronunciation check feature.

  methodology gaplimitationsevidence 5/5
  Keywords: pronunciation limited discussion integration specifics training accuracy metrics assessment potential biases check feature
• Segmental and suprasegmental pronunciation training via AI: An exploration of university students’ perceptions and attitudes (2026) · Technology in Language Teaching & Learning · doi

  The paper does not investigate interaction effects between AI pronunciation training modality (segmental-only versus integrated segmental and suprasegmental) and learner factors such as motivation, anxiety, or prior language learning experience on student attitudes and skill development.

  methodology gaplimitationsevidence 5/5
  Keywords: segmental suprasegmental AI pronunciation training learner motivation anxiety interaction effects

Questions about this gap