Artificial Intelligence Everywhere: A Course to Inform and Inspire Students from All Faculties

Challenge

Dr. Adam White, an Assistant Professor of Computing Science at the University of Alberta, Canada, traces the original idea for an introductory course on machine learning for students from all faculties back 10 years. Despite the University’s strong research and teaching foundation in artificial intelligence (AI), the course was never launched. The idea resurfaced when Dr. Bill Flanagan, the current President and Vice-Chancellor, challenged the Department of Computing Science to expand the reach of its program.

Building on his experience in delivering an AI course on Coursera, Professor White worked with Dr. Alona Fyshe, Assistant Professor in Computing Science and Psychology, to develop a for-credit course designed to introduce AI and Machine Learning (ML) to students from all disciplines. Their goal was to create a course for students from the humanities, sciences, arts, social sciences, business and other faculties, many of whom had no background in statistics or programming. The result was Artificial Intelligence Everywhere, a course designed to showcase AI’s potential within various fields of study.

Professor White and Dr. Fyshe structured the course around two primary objectives:

To de-mystify AI by explaining its origins and how it functions, making it more accessible and less intimidating.
To illustrate how AI and ML can contribute to all academic disciplines and career paths, using concrete examples such as AI applications in agriculture to improve plant development.

Experimentation

Designing the Artificial Intelligence Everywhere course was challenging for Professors White and Fyshe as they had to adapt their content and pedagogical approaches from advanced computer science courses with extensive pre-requisites to an introductory course with no requirements for prior knowledge or experience.

Content: Collaborating with colleagues, they crafted a chronological narrative arc linking the history of AI research and development with technological concepts and skills.

Historical Context: The course begins with an historical outline, looking at definitions and early experiences, including an anecdote about a 17th century chess-playing “robot” in a box that fooled people for 80 years. This example highlights the understanding of AI as human expertise inside a box.
Experts Systems Era: The next phase conveys how expert systems were developed in the 60s, 70s and 80s, including a case study of a doctor who manually programmed a diagnostic tool based on interviews with 20 peers. This section introduces basic programming concepts and discusses the limitations of expert systems.
Modern AI: The structure, functioning, and continuously evolving reality of AI is the third theme, based on the key shift from relying on a select group of human experts to a reliance on massive amounts of data. Guest speakers from education, astrophysics, Indigenous studies and other content areas share real-world AI applications in their disciplines.
Ethical Considerations: Awareness and discussion of ethics in the development and uses of AI are addressed in the final course component.

The course was first offered in Winter 2024 as a hybrid course with 100 in-person and 100 online students. Professor White, who taught the course, actively monitored student reactions to assess if his strategies and metaphors for teaching were reaching the students. Artificial Intelligence Everywhere is scheduled to be offered again in Fall 2025 with enrollment expanding to 100 in-class and hundreds of online students. Most registrants in the Winter 2024 course were first year and mature students.

Teaching: The course is built around biweekly 90-minute lectures, exercises and discussions. The discussions are problem-based as he proposes a challenge, such as what questions you would ask your boss who told you to set up a customer service website, and the students discuss this briefly in pairs. The issue is then explored in class, with students better prepared to participate.

There is no textbook. Lecture slides contain comprehensive explanations rather than point-form notes and feature an extensive selection of visuals to aid comprehension. They are distributed to students.

Assessment: Assessment is done through multiple assignments and a final exam. Students complete six multiple-choice quizzes. Written assignments include three short papers of two or three paragraphs on topics such as how AI uses personal data, which are peer reviewed, and two essays of about two pages from a suggested topic list. The final exam is online or in-person according to student choice.

The assignments are varied to offer challenges to the different capacities of students – those with technical background are more comfortable with the quizzes while those in other disciplines are often more adept at the written assignments.

To ensure accessibility, students who are uncertain about their performance in the course can opt for an ‘exploration credit’ allowing them to receive a Pass/Fail instead of a letter grade.

Results

Student response, in-class and online, was positive with strong engagement in discussions and assignments. A particularly noteworthy reaction came from a student who experienced an “aha” moment upon realizing AI’s relevance in chemistry. This realization echoed the prime goal of the course.

For the Fall 2025 session, the assessments were revised to add more written work to better challenge the technologically knowledgeable students. The first essay will be marked on writing style and the second on content.

Students were allowed to do each quiz as often as they wanted in Winter 2024 and the system recorded their best mark, but the new maximum will be three tries.

Next Improvement Steps

Artificial Intelligence Everywhere is the first course in a five-course certificate being planned. An additional required course will be offered on the ethical considerations and societal implications of AI. Each student will also choose two AI-related electives from their own faculties. The final required capstone course will bring the cohorts back together in interdisciplinary project teams to work on an AI-related project.

Once the first cohort of students complete the capstone project, research will be done on their experiences of the program and how it influenced their perspectives on AI.

The registration goal for the introductory course is 1,000 online students.

Potential

In a development that complements and supports the goals of Artificial Intelligence Everywhere, the University of Alberta plans to hire a cohort of professors in different disciplines, such as education, architecture and physics, who will work with Amii to research and develop applications relevant to their disciplines.

Dr. White also serves as a Director at the Alberta Machine Intelligence Institute (Amii) and a Canada CIFAR (Canadian Institute for Advanced Research) AI Chair. CIFAR, as part of the Pan-Canadian Artificial Intelligence Strategy supports academic talent recruitment and development for research and training at three institutes, including Amii.

Amii collaborates with the University of Alberta through funding, partnerships, programming and other initiatives, providing, for example, support for peer-adjudicated projects such as Artificial Intelligence Everywhere. Currently, the University of Alberta is home to 36 Amii Fellows, 25 Amii Canada AI Chairs and 400 undergraduate, graduate and postgraduate researchers working to produce Artificial Intelligence and Machine Learning advancements across various sector.

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