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-2026-2825
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:30:58.908038Z
status: published
lens: g_space
sector: education
headline: Do Generative AI-Powered Pedagogical Agents Improve Learners’ Academic Performance Effectively? Evidence From Meta-Analysis
dek: With the breakthrough advancements in generative artificial intelligence (GenAI) technology, GenAI-powered pedagogical agents (GenAI-PA) are emerging as a transformative paradigm shift in education. However, there is still debate about whether the use of GenAI-PA is beneficial for learners’ academic performance. Therefore, this study conducted a meta-analysis of 27 experimental and quasi-experimental studies from 2015 to 2025. The results showed that (1) GenAI-PA had a significant effect on learners’ academic pe…
gain_title: A meta-analysis of 27 experimental and quasi-experimental studies found that learners who used generative AI pedagogical agents scored higher on academic performance, with a pooled effect size of g = 0.401.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 27 experimental and quasi-experimental studies found that learners who used generative AI pedagogical agents scored higher on academic performance, with a pooled effect size of g = 0.401.
gain_evidence: GenAI-PA had a significant effect on learners’ academic performance ( g = 0.401)
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis pooling 27 experimental and quasi-experimental studies published between 2015 and 2025 found that generative AI-powered pedagogical agents improved learners' academic performance, with a pooled effect size of g = 0.401.

The size of the effect varied by setting. It was larger in collectivist cultures and in teacher-directed learning than in self-directed learning, and dialogue style mattered: multimodal dialogue showed the highest pedagogical potential.

Grade level, gender, learning domain, learning duration and the instructional role of the agent did not change the effect. The authors discuss design and application recommendations, and say the question of benefit had been debated.
limitation: 
tag: Evidence-backed gain
key_points: The analysis covered 27 experimental and quasi-experimental studies from 2015 to 2025. | The pooled effect on academic performance was g = 0.401, reported as significant. | The effect was greater in collectivist cultures. | Teacher-directed learning showed a significantly better effect than self-directed learning. | Multimodal dialogue showed the highest pedagogical potential among dialogue modalities. | Grade level, gender, learning domain, learning duration and the agent's instructional role did not influence the effect.
rundown: The researchers set out to settle a debate over whether generative AI-powered pedagogical agents help learners' academic performance. They pooled 27 experimental and quasi-experimental studies spanning 2015 to 2025 and found a significant overall effect of g = 0.401.

Moderator analyses showed where the agents worked best. Effects were greater in collectivist cultures, and teacher-directed learning outperformed self-directed learning. The dialogue modality also mattered, with multimodal dialogue showing the highest potential.

No significant moderating effect was found for grade level, gender, learning domain, learning duration or whether the agent played an instructional role. The paper closes with recommendations for designing and applying these agents.
sources:
- peer_reviewed | Journal of Educational Computing Research | https://doi.org/10.1177/07356331251400540 | 2025-11-18
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

1df113ac17024ebb3d50d1c3b6fd6b4157a57576412fa24ea5400fbc7b94bc8e

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000