The workshop aims to foster meaningful dialogue among faculty in Economics and Law about integrating AI into educational practice, highlighting its benefits and potential applications. It will offer strategies to enhance student learning through AI while ensuring adherence to Academic Integrity policies specific to these fields. The facilitator will utilize active learning approaches such as small-group debates, case studies relevant to Economics and Law, and interactive discussions to encourage collaboration and engagement. Participants will be guided to reflect on their teaching methods, exchange ideas with peers, and collaboratively apply AI-driven innovations to their teaching. Ethical considerations, particularly relevant to Economics and Law, will be a focal point of the workshop. Discussions will address issues such as ethical use of data, potential biases in AI algorithms, and preserving the essential human elements of teaching amidst technological integration. Participants will contemplate these ethical dimensions and develop strategies to navigate them responsibly. Finally, attendees will use AI tools to design a course module in their disciplines, which includes crafting learning outcomes, selecting appropriate pedagogical strategies, and aligning assessment tools—incorporating AI effectively in their course design process.

Large Language Models (LLMs) are revolutionizing how humans interact with technology, transforming fields ranging from education and healthcare to creative arts and industry. With unprecedented abilities to comprehend, generate, and reason through human language, LLMs open new possibilities for innovation, problem-solving, and productivity. As these models rapidly evolve, understanding the emerging trends and capabilities becomes crucial to unlocking their full potential across diverse domains. This comprehensive workshop dives deeply into the most exciting and influential developments shaping today’s LLM landscape. Participants will explore advanced reasoning techniques that allow models to handle complex problems, multimodal capabilities enabling integration across text, audio, and visual modalities, and state-of-the-art methods in prompt engineering that maximize model effectiveness. Practical, hands-on sessions will showcase retrieval-augmented generation (RAG), instruction tuning, external tool integration, and extended context windows, highlighting how these innovations substantially improve model accuracy, coherence, and applicability. Additionally, the workshop introduces agent-based systems and agentic workflows, illustrating how LLMs can autonomously orchestrate multiple tasks, reason over outcomes, and collaborate dynamically with other AI systems or external APIs. Participants will learn to design, deploy, and optimize LLM-driven agents capable of achieving complex objectives in real-world applications. Tailored to researchers, developers, and industry professionals, this workshop offers both theoretical insights and practical skills, empowering attendees to lead the next wave of AI-driven innovation.