Teaching AI Literacy to Tertiary Students
As AI technologies become increasingly embedded in daily tasks and professional tools, there is a growing need for tertiary education to equip students with a robust understanding of AI, including its capabilities, limitations, and ethical implications. Here’s how educators can rise to this important challenge.
Integrating AI into the Curriculum
AI literacy should not be confined to computer science departments. Given its widespread application, understanding AI and its societal impacts is essential for all students. Integrating AI education across diverse disciplines can be achieved through interdisciplinary courses that combine AI with fields such as ethics, policy, health sciences, and environmental studies. For instance, a course on AI and ethics could be co-taught by lecturers from philosophy and computer science departments, offering students multiple perspectives on the issues at hand.
Hands-On Learning Experiences
Practical experience is key to understanding any tool, and AI is no exception. Universities can provide labs and project-based learning that involve using AI tools for real-world applications. This could include projects where students use AI to analyse large datasets, create models, or even build simple AI systems themselves. Such hands-on exposure helps demystify AI technology and teaches students about the nuances of working with AI, including how to handle and rectify AI hallucinations — instances where AI systems generate false or misleading outputs.
Focusing on Ethical Implications
AI can perpetuate or even exacerbate biases if not carefully managed. Teaching students about the ethical considerations of AI deployment is crucial. This involves not only discussions about data privacy and surveillance but also about how algorithms can affect fairness, such as in hiring practices or loan approvals. Case studies can be an effective tool here, allowing students to explore real incidents where AI has gone awry and debate the best courses of action in complex scenarios.
Activity Idea – AI in Healthcare
Context
An AI system designed to allocate organ transplants prioritises patients based on survival probability, which disadvantages certain groups.
Teaching Objectives
- Analyse ethical issues in using AI for life-and-death decisions.
- Discuss the balance between efficiency and equity in healthcare.
- Consider the ethical implications of AI access and its impact on healthcare inequality.
Activity
Students create a panel discussion, where they represent different stakeholders (patients, healthcare providers, ethicists) and explore the ethical dimensions of AI in healthcare decisions.
Critical Thinking and Evaluation
While AI can perform many tasks, critical thinking cannot be outsourced. Students must be taught to critically assess AI-generated information, understand its sources, and cross-verify facts, especially in an era where AI tools are common in generating content across media platforms. Courses can incorporate activities that challenge students to identify AI hallucinations and distinguish between AI-generated content and human-generated content.
Collaborative Learning
AI’s complexity and breadth mean that no single discipline holds all the answers. Encouraging collaborative projects between students from different faculties can foster a more comprehensive understanding of AI. For example, art students might work with engineering students to explore how AI can be used in digital media or creative design, highlighting both the technological and creative aspects of AI.
Continuous Adaptation
The field of AI is dynamic, with frequent breakthroughs and evolving capabilities. Educational programs must be flexible and adaptive, regularly updating course content to reflect the latest developments and discussions in the field. This might involve partnerships with AI companies and ongoing training for educators to stay abreast of the latest technologies and teaching methods.
By embracing these educational strategies, universities can prepare students not just to use AI tools, but to improve them, critique them, and leverage their capabilities ethically and effectively. As AI continues to shape various aspects of life and work, tertiary education has a pivotal role in ensuring that future professionals are ready for the challenges and opportunities that lie ahead. This comprehensive approach to AI education will help cultivate a generation of informed, thoughtful, and conscientious users and developers of AI technologies.
Key Points
- Integrate AI Across All Disciplines: AI education should not be limited to computer science but integrated across various fields to help students understand its broad applications and impacts.
- Hands-On Learning: Practical experience with AI through labs and projects enables students to understand its operations and challenges, including dealing with AI hallucinations.
- Ethical Training: Ethical considerations are crucial in AI education, focusing on how AI can influence societal norms and individual rights, such as privacy and bias.
- Critical Thinking Skills: Teaching students to critically evaluate AI-generated information and understand its sources is essential for navigating an AI-enhanced world responsibly.
- Continuous Curriculum Update: AI is a rapidly advancing field, requiring educational content to be regularly updated to include the latest developments and technologies.
