Student engagement grows with AI dialogue in native and English languages
From Written Response to Dialogue with AI: How Activity Format, Interaction Modality, and Language Impact Student Learning and Engagement
Human-Computer InteractionComputers and Society
Summary
Students often write responses for learning, but they might not engage deeply with the material. This study tested using conversation with AI instead of just writing answers, and found that students spent more time and felt more interested when they talked with AI by text or voice. Using both their native language Kannada and English helped some students talk more easily, especially with voice, but many still valued English for their future goals. The study shows that using AI for learning needs careful choices about how students interact and in what language.
What this means in practice
- •For educational software developers: Create AI learning tools that support both text and voice dialogue in native and second languages to boost student engagement and involvement.
- •For language learning platform teams: Design bilingual conversational AI activities that reduce speaking difficulties and improve learner persistence in voice-based language practice.
Authors
Deepak Varuvel Dennison, Connie Zhang, Rene Kizilcec, Aditya Vashistha
Abstract
The widespread availability of LLMs is challenging written learning activities, as students can increasingly generate responses without necessarily engaging with the learning content. Conversational AI creates an opportunity to redesign these activities as dialogue, while multilingual capabilities may make such dialogue more accessible to students learning through a non-native language. We conducted a field study with 305 native Kannada-speaking undergraduate students at English-medium institutions in Karnataka, India. We compared written response activities with text- and voice-based dialogic activities with AI, each conducted in English-only or bilingual Kannada-English settings. Students who completed dialogic activities spent more time on the activities, contributed more, and reported greater interest and self-efficacy than those completing written responses, although fewer students completed the dialogic activities overall. Knowledge increased across all conditions, with no reliable differences in gains between activity formats or languages. Language shaped participation differently across modalities: bilingual interaction was particularly beneficial in voice dialogue, where it reduced articulation difficulties, increased turns demonstrating understanding, and reduced conversation abandonment. However, students also valued English because of its connection to their academic and professional aspirations. These findings show that designing dialogic learning with AI requires more than choosing between writing and dialogue, voice and text, or English and students' native languages. We highlight opportunities to give learners greater control over modality, information, and pace, and to use native languages as translanguaging support rather than as a replacement for English.