Designing Conversational Agents for Adaptive Instructional Support in Business Simulation Gaming

Overview

Wenzel, Geiger, and Liening use Action Design Research to design, build, and evaluate "Lara," an AI-enhanced conversational agent that provides adaptive instructional support within business simulation games (BSGs) for entrepreneurial learning. Grounded in educational psychology and information systems research — spanning cognitive load, self-determination, emotion, and social presence theories — the study produces the CAIS-GBL framework (four design principles, fifteen design features) instantiated in Lara and evaluated across two build–intervention–evaluation cycles. The design is deliberately framed by an equity-by-design stance and the Universal Design for Learning (UDL) principles, aiming to provide adaptive support that removes learning barriers rather than reinforcing educational disparities.

Key Findings

• Consistently positive perceptions across all five dimensions. In the five-week field study (n = 112 users), students rated Lara's cognitive presence, support for self-regulated learning, social presence, perceived ease of use, and behavioral intention all significantly above the theoretical midpoint.
• Perceptions evolved over time. Social presence and perceived support for self-regulated learning increased significantly across the five BSG rounds (e.g., self-regulated learning rose from β = 0.254 in round 2 to β = 0.470 in round 5), while cognitive presence stayed stably high, ease of use declined slightly, and engagement peaked during the simulated crisis in week 3.
• Dispositions, not demographics, shape acceptance. Gender, age, and prior knowledge did not significantly predict CA perceptions, whereas AI attitude and the "Socialiser" player type positively predicted social presence, cognitive presence, and support for self-regulated learning; the "Achiever" type showed no significant effects.
• A needs analysis revealed mostly cognitive hurdles. Of 61 reported hurdles across the BSG, 68.9% were cognitive — shifting from information selection early on to organization/integration for strategic decisions as the game progressed — highlighting demand for formative feedback and on-demand guidance.
• Two iterative evaluation cycles refined the artifact. An alpha workshop with graduate student teachers surfaced improvement areas in cognitive and social presence (e.g., reducing artificial follow-up questions), which informed the beta version while the CAIS-GBL framework was retained.
• Equity-by-design is integral, not incidental. The authors caution that the absence of demographic effects does not guarantee equitable benefit, since attitudinal and motivational profiles still shaped perceptions and may warrant targeted support to prevent disengagement.

Implications for Practice

• Adopt the CAIS-GBL framework as a reusable design template for conversational agents in DGBL, with attention to metric-based formative feedback, frictionless onboarding, empathic interaction, and sociability- and equity-by-design (aligned with UDL).
• Align agent design with learners' AI attitudes and game-related motivations, not only their demographics or prior knowledge, when planning adaptive instructional support.
• Embed equity safeguards into CA architecture, such as grounding feedback exclusively in in-game performance metrics, minimizing personal data, and providing transparent, contestable support to reduce bias risk.
• Operationalize adaptive support at scale in resource-constrained settings, using in-game metrics to deliver timely Feedback and Scaffolding without requiring one-to-one human support.

Connected Concepts

• Conversational AI
• Game Based Learning
• Business Education
• Universal Design For Learning
• Self Regulated Learning
• Simulation
• Adaptive Learning

Connected Articles

• AI Enabled Serious Games — AI and adaptivity in serious games for training
• Conversational Agents Novice Programmers Scoping 2025 — conversational agents and design grounding in learning theory
• Ludia Udl AI Thought Partner 2026 — UDL-aligned AI tool design and equity-by-design
• Adaptive AI Scaffold Collaborative Problem Solving 2026 — adaptive AI scaffolding for collaborative problem solving

Citation

Designing Conversational Agents for Adaptive Instructional Support in Business Simulation Gaming — Wenzel, A., Geiger, J.-M., & Liening, A. (2026). Computers and Education: Artificial Intelligence, 10, 100576.