The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2743
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:17:49.809862Z
status: published
lens: g_space
sector: education
headline: Effects of speech-enabled LLM-based chatbots on L2 learning outcomes and affective domains: a multilevel meta-analysis
dek: Abstract Recent developments in natural language processing and automatic speech recognition have significantly advanced chatbot communication using the voice mode, establishing these tools as viable resources for L2 speaking practice. More recently, the L2 chatbot domain has leveraged large language models (LLMs) to enhance contextual understanding and facilitate meaningful dialogues, prompting research into the effects of speech- and LLM-based chatbots on L2 learning outcomes and affective domains. This study…
gain_title: A meta-analysis of 19 studies (32 samples, 2,643 learners) found speech-enabled LLM chatbots were linked to a pooled effect size of 0.68 on second-language learning outcomes and 0.64 on affective measures.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 19 studies (32 samples, 2,643 learners) found speech-enabled LLM chatbots were linked to a pooled effect size of 0.68 on second-language learning outcomes and 0.64 on affective measures.
gain_evidence: the overall effect size for L2 learning outcomes was 0.68 ( p < 0.001) | The overall effect size for affective domains was 0.64 ( p < 0.001)
problem_reading: (none)
problem_evidence: (none)
quick_read: A multilevel meta-analysis pooled 19 studies, covering 32 samples and 2,643 second-language learners, on chatbots that use voice mode and large language models for speaking practice. The overall effect size was 0.68 for learning outcomes.

For affective domains, the overall effect size was 0.64, and both results were statistically significant (p < 0.001). Affective domains cover the emotional side of learning.

Moderator analyses showed that learner proficiency, educational level, interaction format and the context of chatbot use significantly influenced how effective the chatbots were, so the average gain does not apply equally to every learner group.
limitation: 
tag: Evidence-backed gain
key_points: The analysis pooled 19 studies with 32 samples and 2,643 learners in total. | The overall effect size for second-language learning outcomes was 0.68 (p < 0.001). | The overall effect size for affective domains was 0.64 (p < 0.001). | Learner proficiency significantly moderated how effective the chatbots were. | Educational level, interaction format and context of chatbot use also significantly influenced effectiveness.
rundown: The study is a multilevel meta-analysis, a method that handles multiple samples drawn from the same study. It looked at chatbots that combine automatic speech recognition with large language models to support spoken practice in a second language.

Two outcome families were examined: learning outcomes and affective domains. Pooled effects were 0.68 and 0.64 respectively, both significant at p < 0.001.

Moderator analyses found that learner proficiency, educational level, interaction format and the context in which chatbots were used shaped the size of the effect.
sources:
- peer_reviewed | International Review of Applied Linguistics in Language Teaching | https://doi.org/10.1515/iral-2026-0172 | 2026-10-08
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

3941b6b326fefa35eccfb20fa855ec219da70dbd0ced23659fb9fb2ddc44213a

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000