In an era defined by digital transformation, the intersection of artificial intelligence (AI) and human collaboration presents unprecedented opportunities and complex challenges. This course provides a comprehensive exploration of the theoretical framework behind human-AI collaboration, guiding students through the key concepts, ethical considerations, and technical foundations necessary to understand and participate in this evolving field. By focusing on the theoretical underpinnings of AI, this course offers a structured pathway for students to grasp the nuances of collaborative AI, including its impact on the modern workplace, and the broader implications of this technology for society.

At the heart of the course is an in-depth examination of collaborative AI within today’s workplaces. Beginning with an overview of how AI shapes and augments human productivity, students are introduced to the many facets of AI’s role in supporting human work across industries, from streamlining workflows to enhancing decision-making processes. Through this study, students are encouraged to think critically about the benefits and potential challenges inherent in AI integration, gaining insight into the shifts in work dynamics and operational efficiency driven by AI-powered tools. As these concepts are introduced, the course unpacks key terminology, ensuring students are well-versed in the vocabulary and technical language that frame collaborative AI discussions, making it easier to engage in informed dialogues about AI and its implications.

Further into the course, students delve into the basics of AI and machine learning, exploring the various types of AI—including narrow, general, and superintelligent forms—and understanding how each type impacts human collaboration differently. This theoretical foundation allows students to differentiate between different applications and capabilities of AI systems and appreciate the role of machine learning, deep learning, and natural language processing (NLP) in developing AI tools that support human-centered design. The course’s approach to AI and data-driven insights fosters a nuanced understanding of the importance of data quality and algorithms, highlighting how ethical data use and accountability play a critical role in sustainable AI development.

Human-centered AI design is another focal point, introducing students to principles that prioritize user experience and empathy in AI interactions. Emphasizing the significance of balancing automation with a human touch, this course provides students with the tools to analyze and critique AI designs from a usability perspective, questioning how AI can be designed to minimize biases and promote inclusivity. By understanding how to navigate human biases in AI systems, students develop the skills needed to evaluate and advocate for designs that enhance, rather than replace, human effort and intuition. As part of this human-centered approach, the course examines methods for usability testing in AI, underscoring the importance of aligning AI applications with the values and needs of the people they serve.

The course also prepares students to work within collaborative AI teams, examining the distinct roles, skills, and team structures required to drive AI projects. Students learn about the diverse competencies needed for successful AI collaboration, from technical expertise to effective communication across disciplinary boundaries. This section emphasizes the importance of communication in cross-functional teams, showing students how successful collaborative AI initiatives are often rooted in clear communication and well-defined roles. In this way, the course equips students with the knowledge to contribute to or lead teams where human and AI contributions are interwoven.

As students advance, they explore how AI supports decision-making in professional contexts. Here, the course distinguishes between decision-support and decision-autonomy, fostering an understanding of how AI can enhance human judgment without entirely replacing it. This leads to an investigation into cognitive bias and the role it plays in both human and machine decision-making, encouraging students to adopt a critical stance on the use of AI in sensitive decision-making scenarios. Issues of trust and accountability are examined to underscore the importance of transparency in AI, especially in systems that heavily impact human lives, such as healthcare and finance.

Ethics and governance are central themes in the course, as students explore the regulatory landscape and ethical principles that guide AI development and usage. By learning about AI transparency, fairness, and inclusivity, students gain a well-rounded perspective on the governance frameworks necessary to implement responsible AI systems. These discussions extend to the role of AI in societal shifts, allowing students to reflect on the profound changes AI may bring to human identity, values, and relationships. Such reflections encourage students to think beyond the technical aspects and consider the societal and philosophical implications of AI integration.

Finally, the course delves into the future of collaborative AI, examining trends in augmented intelligence, new work models, and the long-term considerations of AI integration in businesses and society. By exploring these forward-looking topics, students gain insight into how AI might shape the next generation of workplaces, redefining roles and relationships between humans and machines.