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TRUVACE RECORD VERSION
record: TRV-2026-2477
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T00:02:24.461961Z
status: published
lens: g_space
sector: business
headline: AI-Assisted Negotiation Training: A Quasi-Experimental Examination of Buyers’ and Sellers’ Perceived Negotiation Performance in a Used-Car Sales Simulation
dek: Background: Evidence on artificial intelligence (AI) as a preparatory learning aid for novice human negotiators remains limited in consumer negotiation simulations. Existing work emphasizes automated agents rather than students preparing to negotiate. Against this backdrop, the present study examined changes in students’ perceived negotiation performance, differences in those changes between buyer and seller roles, and the purposes for which students used AI during a simulated used-car sale. The outcome was perc…
gain_title: In a 74-student quasi-experiment in Bangkok, undergraduates rated their own negotiation performance 4.45 out of 5 after AI-assisted preparation versus 3.78 without it, a 0.67-point rise.
problem_title: (none)
trace_subject: (none)
gain_reading: In a 74-student quasi-experiment in Bangkok, undergraduates rated their own negotiation performance 4.45 out of 5 after AI-assisted preparation versus 3.78 without it, a 0.67-point rise.
gain_evidence: Students reported higher perceived negotiation performance after AI-assisted preparation (M = 4.45, SD = 0.42) than after the preceding non-AI round (M = 3.78, SD = 0.58).
problem_reading: (none)
problem_evidence: (none)
quick_read: A study of 74 undergraduate business students at a Bangkok university found that students rated their negotiation performance higher after preparing with generative AI. Self-ratings averaged 4.45 after AI-assisted preparation, compared with 3.78 in an earlier round without AI, a 0.67-point difference with a large effect size (|d| = 1.07).

Each student negotiated a used-car sale twice within 24 to 48 hours, keeping the same buyer or seller role and the same scenario. Before the second round they spent about five minutes using AI tools of their choice. Ratings rose by 0.68 points for buyers and 0.66 points for sellers, and the study found no significant difference between roles.

The outcome was perceived performance, not agreed prices, fairness or independently judged negotiation quality. Because every student did the non-AI round first and then repeated the same scenario, practice and familiarity may account for part of the higher second-round ratings.
limitation: Every student did the non-AI round first and repeated the same scenario, so practice and familiarity may have contributed to higher second-round ratings; the results reflect self-reports, not a causal estimate of AI's effect on objective negotiation performance.
tag: Evidence-backed gain
key_points: The study enrolled 74 undergraduate business students in a negotiation course in Bangkok: 37 buyers and 37 sellers. | The first negotiation had no AI help; before the second, students spent about five minutes preparing with generative AI tools of their choice. | Each negotiation lasted roughly five to seven minutes, and performance was rated on five-item self-report scales. | Mean self-rated performance rose from 3.78 to 4.45, a 0.67-point gain, t(73) = −9.23, p < .001. | Buyers' scores rose 0.68 points and sellers' 0.66 points, with no significant condition-by-role interaction. | Students most often used AI for information analysis (91.9%), then strategy planning (60.8%), role-play practice (51.4%) and feedback (40.5%).
rundown: The design was a single-group, repeated-measures quasi-experiment. Students completed both negotiations outside regular class hours, keeping their assigned roles and using the same used-car scenario. Paired-samples t-tests compared the two conditions, and a mixed-design ANOVA tested differences across buyer and seller roles.

The ANOVA showed a significant condition effect, F(1, 72) = 83.97, p < .001, partial η² = .54. The gain was almost identical for buyers and sellers, but the authors say this does not mean the groups had equal bargaining power or equivalent economic outcomes.

Students described what they used AI for: information analysis (91.9%), strategy planning (60.8%), role-play practice (51.4%) and feedback (40.5%). The authors say these patterns do not establish which AI function was effective, and they suggest that heavier use of analysis than of interactive practice points to an area for instructional development.
sources:
- peer_reviewed | Suranaree Journal of Social Science | https://doi.org/10.55766/sjss286187 | 2026-11-11
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