Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison

Mohammed Azeez Khan; Aaron Dsouza; Ashutosh Mishra; Ummul Faiz Z. Bibi; Neha K. Nair; Amar Kumar Behera

cs.LGOct 9, 2026

Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison

Authors: Mohammed Azeez Khan, Aaron Dsouza, Ashutosh Mishra, Ummul Faiz Z. Bibi, Neha K. Nair, Amar Kumar Behera

Organizations: Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, India · Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Warangal, India · Department of Computer Science and Engineering, Nawab Shah Alam Khan College of Engineering and Technology, Hyderabad, India · Department of Physics, National Institute of Technology Warangal, Warangal, India · Department of Design, Indian Institute of Technology Kanpur, Kanpur, India

Abstract

Large language models (LLMs) are increasingly being explored as facilitators of group discussion and collaborative work, but the behavioral differences between LLM-generated and human facilitation remain insufficiently characterized. We compare human Project Manager facilitation turns from scenario-based collaborative design meetings with Llama 3.1 8B Instruct responses across 199 paired contexts from the AMI Meeting Corpus. The analysis covers five confirmatory dimensions: contextual semantic distance, a lexical elaboration proxy, empathyand-hedge lexical density, topic divergence, and phase-vocabulary alignment. An exploratory directiveness measure failed domain validation and is not interpreted. The sampled human Project Manager turns were higher on contextual semantic distance (r = 0.644) and on empathyand-hedge lexical density (r = 0.203). The second contrast is carried mainly by hedge terms, so we read it as lexical hedging rather than psychological empathy. Llama-generated facilitation was higher on topic divergence (r = 0.703), the lexical elaboration proxy (r = 0.288; lengthconfounded), and instruction-conditioned phase-vocabulary alignment (r = 0.382). A secondary no-phase check left the Llama-higher phase contrast in place. The system prompt still requested a facilitation move, so that check is not evidence of spontaneous phase awareness. These differences should not be interpreted as a ranking of facilitation quality. The resulting behavioral profile provides a reference point for later comparisons of LLM facilitators, human facilitators, prompts, models, and collaborative settings.

Figures & tables

Figure 1: Primary effect sizes at $T=0.7$ ( $n=199$ pairs). Points are the paired effect size $r$ , signed so that negative values mean the human turn is higher and positive values mean the Llama response is higher. Intervals are the 95% bootstrap confidence intervals on $r$ , given the same sign. Directiveness is omitted because it is unvalidated.

Dimension	Observed difference	What it supports	What it does not establish
Contextual semantic distance	Human higher	Greater distance in all-MiniLM-L6-v2 space	General semantic superiority
Lexical elaboration	Llama higher	Greater surface lexical elaboration	Conceptual specificity
Empathy/hedging	Human higher	A lexical hedge/empathy difference	Psychological empathy
Topic divergence	Llama higher	Greater LDA topic-distribution divergence	Creativity
Phase alignment	Llama higher	Greater phase-vocabulary alignment	Spontaneous phase awareness

Table 1: What the five confirmatory contrasts support. Directions are human versus Llama 3.1 8B Instruct at

T=0.7

Source	N	Words	SD	Types	Sents
Human PM	199	23.5	12.7	725	1.5
LLM $T=0.3$	199	40.5	7.9	1031	1.8
LLM $T=0.7$	199	38.5	7.5	1125	1.6
LLM $T=1.0$	199	37.6	8.5	1196	1.5

Table 2: Descriptive statistics for human PM turns and Llama 3.1 8B Instruct responses. Types are unique lowercased word tokens. Mean sentences count terminal punctuation.

Dimension	H Mdn	L Mdn	$r$	95% CI	$p_{\text{Bonf}}$	Sig.
Contextual distance	0.742	0.593	0.644	[0.56, 0.72]	$<$ 0.001
Lexical elaboration	0.397	0.416	0.288	[0.16, 0.40]	$<$ 0.001
Empathy/hedge density	0.000	0.000	0.203	[0.07, 0.34]	0.021
Topic divergence	0.540	0.744	0.703	[0.63, 0.76]	$<$ 0.001
Phase-vocabulary alignment a	0.000	0.000	0.382	[0.25, 0.50]	$<$ 0.001
Directiveness (unvalidated) b	0.021	0.036	0.026	[0.00, 0.17]	0.718	ns

Table 3: Paired Wilcoxon signed-rank tests: human PM turns versus Llama 3.1 8B Instruct (

T=0.7

n=199

pairs).

p_{\text{Bonf}}=\min(1,5p_{\text{raw}})

for the five confirmatory dimensions and is compared with

0.05

(equivalently, raw

p

with

0.010

). Effect size

r=|Z|/\sqrt{n}

. The 95% confidence interval is a bootstrap with 2,000 resamples.

Figure 2: Score distributions for human facilitation and Llama 3.1 8B Instruct at $T=0.7$ . Horizontal lines are medians. Hedge/Empathy is lexical density, not a psychological rating. Directiveness is exploratory and unvalidated and is not a confirmatory dimension.

Figure 3: Phase-vocabulary means at $T=0.7$ . Removing the explicit phase label did not eliminate the Llama-higher contrast. The label-present versus no-label difference was not statistically significant after correction. The system prompt still requested a design-facilitation move.

Contrast	Means	$r$	95% CI	Raw $p$	Sec. $p$
Human vs. no label	0.0050 vs. 0.0115	0.294	[0.16, 0.42]	$<$ 0.001	$<$ 0.001
Label vs. no label	0.0149 vs. 0.0115	0.123	[0.01, 0.26]	0.082	0.163

Table 4: Secondary phase-label sensitivity at

T=0.7

(

n=199

matched contexts). The two raw

p

-values are Bonferroni-adjusted together. This family is separate from the five confirmatory dimensions. Effect size

r=|Z|/\sqrt{n}

, with the same 2,000-resample bootstrap as the primary tests.

Dimension	$\chi^{2}$	$p$	$p_{\text{Bonf}}$	Sig.
Contextual distance	0.44	0.802	1.000	—
Lexical elaboration	4.04	0.133	0.664	—
Empathy/hedge density	2.21	0.332	1.000	—
Topic divergence	5.46	0.065	0.327	—
Phase-vocabulary alignment	5.61	0.060	0.302	—
Directiveness (exploratory)	0.85	0.654	0.654	—

Table 5: Friedman tests of scores across

T\in\{0.3,0.7,1.0\}

within the same 199 contexts. Bonferroni correction uses the five confirmatory dimensions.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 4: Spearman correlations among the six scores ( $N=796$ ), including the unvalidated directiveness score.

Figure 5: Exploratory PCA of behavioral scores for human turns and Llama responses at all three temperatures, fit on all 796 turns. The axes are principal components of the score matrix, not a validated style space.

Figure 6: Descriptive Spearman correlations between decoding temperature and scores on 597 stacked responses. These are not the within-context Friedman tests.

Figure 7: Mean scores for human facilitation and Llama responses at $T=0.7$ . Each panel starts at zero and uses its own scale. Printed numbers are the raw means. Hedge/Empathy is lexical density. Directiveness is exploratory and unvalidated and is not a confirmatory dimension.

Dimension	Prim. $r$	Len. $r$	Part. $\rho$	ME $\beta$	$p_{\text{Bonf}}$	Note
Contextual distance	0.644	0.514	$-$ 0.137	$-$ 0.143	$<$ 0.001	Stable
Topic divergence	0.703	0.712	$-$ 0.027	+0.199	$<$ 0.001	Stable
Lexical elaboration	0.288	0.807	$-$ 0.596	+0.017	$<$ 0.001	Length-confounded
Phase alignment a	0.382	0.487	$-$ 0.059	+0.010	$<$ 0.001	Instruction-conditioned
Empathy/hedge density	0.203	0.231	+0.068	$-$ 0.011	0.021	Hedge proxy
Directiveness (exploratory)	0.026	—	—	+0.005	0.718	Unvalidated

Appendix

Table 6: Robustness checks: length-matched pairs (

n=73

), Spearman correlation of the paired difference with human word count, and a mixed-effects estimate with a random intercept for context. Primary

r

is from the paired Wilcoxon test (

n=199

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