Winter School Modules

This Winter’s pedagogical program offers three specific tracks for graduate management and engineering students, and working professionals aiming to master this next generation of AI applications.

Highlights of the 2026 Winter School include an introductory seminar on contextual AI, a workshop on building agentic systems integrating human and machine intelligence, workshops exploring leveraging these systems in production, transportation, and communication, and a deep dive into the UX/UI challenges of agentic systems.

Context is all you Need

The opening seminar explores the critical importance of adapting generative AI models to specific local contexts across enterprise environments. Participants will gain strategic insights into the foundational Module Context Protocol that enables effective communication between human and machine agents within organizational structures. The session examines how Codex adaptation frameworks help localize models to specific industry needs, regulatory environments, and operational workflows, while examining how OpenAI agents with contextual awareness can transform business operations.

From Case Studies to Use Cases

This hands-on workshop will cover the necessary topics to build business-ready applications of contextual AI in the fields of communication, transportation and production. Participants will review the case studies developed in the BAI white paper, a,d model and an agentic system applying in-context learning and positional encoding techniques to address specific challenges, and document realistic use cases moving forward.

Building Agentic Systems

This workshop is designed to help the participants work with large language Models and application frameworks in developing AI agents that can sense, reason, communicate and act. Under the supervision of a Corporate Project Manager, participants will model realistic scenarii for Executives will learn strategies for balancing standardized AI capabilities with localized adaptation requirements, creating governance structures supporting continuous contextual learning, and developing metrics to evaluate contextual performance in production environments.

Enhancing the Explainability of AI models

This workshop addresses the critical importance of explainability, transparency and trust in AI, exploring how responsible AI practices can enhance the performance of agentic systems while aligning with the requirements of current regulatory frameworks. Participants will gain hands-on experience applying best practices to AI use cases and insights into the value of AI transparency, which provides visibility into the workings of AI systems, as well as AI explainability, which is a prerequisite to leveraging human intelligence

Designing the UX/UI of Collaborative Intelligence

Industry experts will address specific UX/IO challenges of agentic systems. Designing systemic features that enhance trust, explainability, and transparency are not just nice-to-have features, they are critical success factors in building contextual AI. Using examples from their current assignments, the facilitators will discuss the challenges of modeling agentic systems to capture, amplify, and communicate the context cues critical to improving personal and organizational decision-making.