AI at the Love School of Business

Students are introduced to how AI is shaping the subject they are studying and learn how to use it appropriately within the course. They may use university-supported tools such as Gemini, NotebookLM or course-specific AI tutors to explore concepts, ask questions and strengthen their understanding, while still being responsible for their own thinking and work.

AI becomes part of how students complete meaningful coursework. Depending on the discipline, students may use tools such as Gemini, Claude, Google Colab, MindBridge or other professional platforms to support research, analyze data, troubleshoot code, test ideas or strengthen recommendations. The focus is on learning how to use AI effectively while evaluating the quality and limitations of what it produces.

Students move beyond using AI as a supporting tool and begin building with it. They may create AI-powered applications, agents, automated workflows, models or other solutions designed around real business problems, using platforms and development tools appropriate to the course. These experiences ask students to combine technical capabilities with business knowledge, creativity and judgment.

Students use professional tools such as MindBridge to explore AI-assisted audit analysis and EY ARC modules to examine AI ethics and generative AI governance. They also work with tools such as NotebookLM and, in advanced coursework, may build AI agents, helping them understand both how AI can support accounting work and where professional judgment still matters.

Students work with Python, Google Colab and generative AI platforms to analyze data, build models and create AI-enabled applications. As they progress, they move beyond using AI for assistance and begin designing solutions that combine analytics, automation and business decision-making.

Students use Stata, custom AI tutors and generative AI tools to support coding, econometric analysis, research and economic modeling. They may use AI to work through journal articles, troubleshoot code or generate economic scenarios, while learning to verify sources, test assumptions and interpret results independently.

Students work with Claude Code, GitHub, VS Code and Rotman software to analyze financial data, build projects and test algorithmic trading strategies. They also use tools such as NotebookLM for equity research connected to real investment decisions, giving them experience with the kinds of analytical workflows increasingly used in finance.

Students use ChatGPT, Gemini, Claude, Google Colab and AI agents to automate workflows, analyze data and explore strategic decisions. They also encounter custom AI-powered experiences, such as live negotiation simulations, that require them to respond to changing conditions and decide when AI can assist versus when human judgment needs to lead.

Students use AI in Google Ads and social media advertising simulations, client analytics projects and sales preparation. Tools such as ChatGPT, Apollo and Synthesia can support prospecting, campaign optimization and communication, while students remain responsible for evaluating the output and making the final strategic decisions.