Define the skill before the technology.
Negotiation practice has a clear activity: a learner tries a strategy in a conversation and considers the response. ASU News describes the Digital Negotiation Assistant’s spoken interaction and dashboard, including concessions, tone shifts, and tactics.
A new training platform needs the same clarity about its own objective. Identify the skill, scenario, learner, and moment of use. A general chatbot has no inherent understanding of what your program considers successful practice.
Connect the interaction to feedback.
The exercise should give the learner a useful next step. Feedback needs to relate to what happened, and the interface needs to make it understandable. Decide what the system can assess and what should remain with the instructor or program team.
TARIY’s engagement for DNA covers full platform development. The independent ASU feature confirms the partnership and describes TARIY’s AI contribution. These are complementary sources of information, not a claim that the feature verifies every implementation detail.
Design for the complete program context.
A working interaction is one part of the product. Learner access, scenarios, permissions, feedback displays, and any connections to existing systems belong in the requirements. A standalone exercise and a platform serving several programs have different scopes.
Subject-matter experts define the teaching content and intended outcomes. Product and engineering work turn those requirements into an experience the program can evaluate. Keep those responsibilities explicit.
Evaluate the product and the learning separately.
Test whether the interaction works, the feedback is useful, and the application handles failures appropriately. Then determine how the program will evaluate learning. A responsive interface or repeated use does not by itself prove educational impact.
ASU reports classroom testing in 2024 and a formal launch in 2025. This article does not claim measured learning gains, adoption totals, or financial returns that have not been supplied and verified.
Start with one exercise you can evaluate.
Bring a scenario, a learning objective, examples of useful feedback, and the systems the experience needs to fit. Agree on a focused first release or pilot before broadening the platform.
TARIY combines AI platform development with education software modernization experience from SMART Flex. We can discuss the product, integrations, evaluation needs, and an appropriate next scope in a 30-minute project fit call.