AI-aware pedagogy for business courses

Almost every discipline and profession has been impacted by the emergence of GenAI tools. Many students are wary of using these because of a lack of understanding of the underlying technology and rampant misinformation about AI, hence the need to implement AI-aware pedagogy that integrates an understanding of AI into the curriculum. Students must see past the demonisation of AI technologies and understand that AI tools can help increase productivity and decision-making in their profession. In addition to comprehending the underlying technology, the student must be able to assess AI technologies critically. They must be able to judge the effect and usefulness of AI tools and the legal, ethical and privacy issues important for AI adoption.

Here are some key ideas on how teachers can design and implement AI-aware pedagogies.

Include the understanding of course-specific AI-related skills in learning outcomes: to do this, conduct lectures and demonstrations of specific AI tools to build awareness and literacy and develop or curate resources such as case studies, articles and videos to explain AI concepts and how to use them. For instance, a course on business analytics could include a class on how students can use AI to predict business outcomes such as customer churn. Your content could focus on the workings of common machine learning algorithms like neural networks, the decision tree classifier or the Bayesian classifier. You could require students to understand how to evaluate the classifier’s results in terms of accuracy and time taken to make the decision.

Design learning activities that facilitate critical evaluation of AI tools: once students understand the technology, they should also be able to assess its advantages and limitations. How accurate is a GenAI tool in classifying customers as likely to churn? A student working with an AI-powered business churn predictor should understand that it can turn out in false positives or false negatives and must be able to map probabilistic results to a business decision. Students should ask themselves how the prediction can be used to design and make offers to customers to prevent them from leaving the business. Facilitate classroom discussions on such topics to help students develop their critical thinking and decision-making skills.

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Use learning activities to help students assess AI tools’ legal, ethical and societal implications: the data collected by GenAI tools must comply with country and company-specific data privacy requirements and students should be able to identify and question the data sources used by them. For instance, students must know the impact of data collected from the internet on personal privacy. They should question whether websites have declarations of their privacy policy and identify their stands on web scraping. You can use content on privacy laws such as GDPR and discussions, debates and brainstorms to teach students about the legal and ethical implications of using data scraped from the internet.

Set formative assessments to assess students’ understanding and critical thinking: use quizzes and group discussions to assess whether students can demonstrate an understanding of and critically analyse GenAI tools. You can also set simple projects that involve students in identifying a business objective, collecting data, training an AI model and presenting their findings. For instance, the students can build an AI-based customer recommender system (a tool that uses machine learning to suggest items to a user) by using readily available datasets on customer purchases. Students should be able to justify the personalised recommendations based on previous purchases and the recommender software. You can use this assessment and student feedback to ascertain whether the relevant learning outcome has been met.

We need to educate students about AI and empower them to think critically and ethically about AI adoption, to prepare them for a future in which it will play a significant role.

Rohini R. Rao is an associate professor at the department of data science and computer applications, Manipal Institute of Technology, India.

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