To Facilitate or not to Facilitate: Human and LLM Facilitator Tendencies in Online Discussions

Dimitris Tsirmpas, Katerina Korre, John Pavlopoulos — arXiv preprint (2026).

Synthesis

This study asks when (not just how) LLMs should facilitate online discussions, creating PEFK, a corpus standardizing and aggregating facilitation datasets, and running the first survey on facilitation timing with expert facilitators and LLM-as-a-judge models.

Key asymmetry: humans are more cautious while LLMs are excessively eager to facilitate, although both are more certain when judging that facilitation is not needed.

Corrective attempts found trained ModernBert classifiers more reliable than alternative LLM setups, though existing datasets impose a relatively low performance ceiling — a benchmark-quality finding for automated discussion facilitation.

For online learning, the work informs when AI should intervene in discussion forums (MOOC-style and classroom), connecting facilitation timing to engagement and moderation research.

Key Findings

The study defines facilitation timing as the decision of when to intervene in a discussion, observing that not intervening when needed can lead to topic derailment or escalation, while intervening when a discussion is going well can irritate participants.

The authors create PEFK, a corpus standardizing and aggregating all relevant facilitation datasets, enabling direct comparison across prior work.

They are the first to run a survey on facilitation timing, executed with expert facilitative participants and LLM-as-a-judge models.

Humans are more cautious about intervening, while LLMs are excessively eager to facilitate; both are more certain when judging that facilitation is not needed.

Trained ModernBert classifiers perform more reliably than alternative LLM setups for predicting facilitation needs, although current datasets impose a relatively low performance ceiling.

Study Design & Method

Automating facilitation has been attempted with encoder-only classifiers, and LLMs have more recently been championed as the eventual solution; however, prior work indicated LLM facilitators are too eager to intervene, rendering them unusable as autonomous agents — a finding the authors contrast with human tendencies for the first time. The study operationalizes what facilitation is, observes when humans decide to facilitate, and compares those decisions with LLM decisions. Corrective alternatives (different LLM setups) and classifier training on established datasets are then evaluated against the aggregated PEFK corpus.

Implications for AI in Education

For online learning environments — MOOC-style forums and classroom discussion spaces — the work clarifies that the timing of AI intervention is as important as its content. LLMs' excessive eagerness to facilitate suggests autonomous moderation agents need calibration toward human caution, and the modest ceiling of existing datasets indicates that better annotation infrastructure is needed before facilitation timing can be reliably automated. The findings connect facilitation timing to Collaborative Learning and to Human In The Loop AI design in educational discourse platforms.

Connected Concepts

Human In The Loop AI

Collaborative Learning

Administrator

Pedagogical Agent

Automated Question Generation

AI Ed Evaluation

Affective Computing

Prompt Engineering

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Citation

Tsirmpas, D., Korre, K., & Pavlopoulos, J. (2026). To facilitate or not to facilitate: Human and LLM facilitator tendencies in online discussions. arXiv:2607.28643.