Every MACI course combines AI fluency with human collaborative skills. Some courses foreground tools, some foreground people, and AI shows up in the content of every course. The eight required courses divide into two categories: "AI Tools & Systems" and "The Human Edge."
Students typically transfer in six undergraduate credits from courses that include substantial writing, capstone, or portfolio-based work. This prior work becomes starting material for assignments in MACI courses. The entire MACI program, including the six undergraduate credits that typically transfer in, is 30 credits.
If you are unsure whether you have six suitable undergraduate credits for transfer into MACI, please reach out to the MACI team, and we will work with you to identify appropriate coursework and explain your options.
This course provides a focused exploration of current and emerging AI tools, platforms, and ecosystems, equipping students to identify and evaluate their potential applications. Students will gain a practical understanding of how to leverage diverse AI technologies to solve real-world challenges. Potential examples include AI-assisted research, data analysis, and data visualization; and AI-driven application development. The course will also examine the evolving landscape of AI, with a focus on emerging trends and their potential impact on various industries.
Throughout, students will build digital resilience — the capacity to adapt their skills as the AI landscape changes. By the end of the course, students will be able to critically assess AI technologies and make informed decisions about their adoption and implementation.
This course addresses the fundamental challenge of aligning AI with human values. We will explore classic and contemporary theories of value and determine why it is difficult to convey these values to a machine.
The course covers the three main types of alignment failure, corresponding to the three main machine learning training methods: bias amplification (in supervised learning), hallucination (in unsupervised learning), and reward hacking (in reinforcement learning).
Students will learn to identify, mitigate, and communicate about AI bias, examining real-world scenarios where the tolerance for bias differs, from medical diagnostics to image generation. The course will explore various fairness metrics, their limitations and trade-offs, and how to apply them effectively.
This course develops the ability to design effective workflows that integrate human collaboration and AI capabilities. Students develop project coordination skills in the context of AI, exploring how work can be orchestrated across human teams and AI systems while maintaining appropriate oversight.
Research on collaboration provides theoretical lenses for designing for teamwork in technology-mediated settings. Human-centered design methods — including contextual inquiry, participatory design, and usability testing — are introduced to facilitate discovery of what people need and to iteratively improve workflows and project coordination. Students develop practical skills through hands-on projects and acquire tools and techniques that will be useful across a wide range of professions.
This course considers the ethics of AI development and deployment. Possible topics include the environmental footprint of AI, data ethics and privacy, intellectual property and training data, algorithmic bias, and AI as a tool for the common good. Further topics may include the global AI divide, autonomous weapons and the militarization of AI, accountability for AI-related harms, impact on vulnerable populations, AI and the transformation of work and society, artificial consciousness and machine rights, and potential catastrophic risks. The course is a roadmap of ethical issues surrounding artificial intelligence.
This course surveys workplace communication skills and issues, focusing particularly on inherently interpersonal communication and AI's impact on this. The course takes up topics such as: dyadic communication, and how common problems in interpersonal communication can be managed; task-oriented small groups, including differentiating roles, motivating and empowering others, and managing discussion; organizational communication, including understanding leadership structures and cultural diversity; conflict resolution through conceptual clarification; and ethical issues in workplace communication posed by the AI environment.
This course develops skills of effective persuasion in the professional world. It addresses topics such as: understanding and distinguishing different types of evidence, evaluating the quality of information sources (including sources supplied by AI tools), and structuring argument outlines; writing argumentative prose, with attention to definition, to matching narratives with audiences, and to storytelling as a means of vivifying argument; and developing oral presentation skills, with attention to engaging in respectful and productive dialogue with others. Throughout, consideration is given to identifying assistance AI tools can provide, as well as to assistance they cannot provide, in the effort to craft persuasive cases.
This course examines research-based frameworks for understanding how learning develops in individuals and groups. Contrasts between human learning and machine "learning" are discussed. Students explore how AI can support — or unintentionally undermine — deep understanding and judgment: through tools such as case analyses, guided prompting exercises, transcript studies, and reflective practice, they acquire habits that help them recognize when interactions with AI tools reinforce learning and when they bypass it. Practical strategies for remaining cognitively active while using AI tools are surveyed, including protocols for evaluating AI-generated output, and for making informed decisions about when and how to rely on AI tools for support. Course assignments incorporate polished writing produced prior to this class, e.g. in capstone undergraduate courses.
This course explores ways in which persons and groups experience and respond to change, especially technological change, and considers navigational strategies, such as viewing disagreement as information rather than obstruction, distinguishing between productive resistance and avoidance, making tradeoffs explicit, and recognizing how power, roles, and incentives shape responses to change. Possible topics include ways in which historical precedents illuminate present-day challenges with AI; contributions from psychology to understanding how identity, emotion, and bias shape responses to change; decision-making under uncertainty; sustaining meaning when familiar structures are disrupted; and ways in which storytelling – constructing overarching narratives that impart meaning or highlight the common good – can help direct change. Course assignments incorporate polished writing produced prior to this class, e.g. in capstone undergraduate courses.
This online program features flexible start times throughout the year. With eight required courses, students may opt to take two classes at once during each seven-week session for an accelerated pace or choose to spread out the coursework for a slower pace. Here are some possible options. You will work with your advisor to choose a course sequence that fits your schedule. Each course lasts about seven weeks.
Total time for completion: 7 months
This plan illustrates one possible course sequence; your own start term depends on when you enroll.
Semester 1 (summer)
Semester 2 (fall)
Total time for completion: 15 months
This plan illustrates one possible course sequence; your own start term depends on when you enroll.
Semester 1 (summer)
Semester 2 (fall)
Semester 3 (spring)
Semester 4 (summer)
Throughout the program, students develop a professional portfolio featuring an artifact from each course. The portfolio provides tangible evidence of your skills that you can share with current or prospective employers.
Throughout the program, students develop a professional portfolio featuring an artifact from each course. The portfolio provides tangible evidence of your skills that you can share with current or prospective employers.
Ready to learn more about earning an M.A. in Collaborative Intelligence at the University of St. Thomas? Request information or attend an information session. We look forward to hearing from you!