A Model to Enhance Students’ AI Literacy

• The AI in Teaching and Learning framework at Queen Mary University of London provides staff with structured guidance on how to advance students’ AI skills in curricular and extracurricular activities.
• Faculty use a bottom-up approach so that students build their skills gradually, from acquiring basic knowledge to testing practical uses to engaging in critical evaluation and creative application.
• The framework promotes an inclusive learning environment where all students have access to AI tools and opportunities to explore AI’s capabilities, limitations, and ethical considerations.

 
The emergence of ChatGPT put AI’s role in the classroom at the forefront of academic discourse. At first, many faculty contested its use in educational settings. But over the last year, that opposition has dissipated, as higher education institutions face increased pressure to improve students’ AI literacy for the sake of future employability.

Indeed, there remains a gap in AI literacy on campuses among both staff and students. While some frameworks have been developed to address that gap—such as the UNESCO AI Competency Framework for Teachers—few target teaching and learning.

With this in mind, the two of us have developed the AI in Teaching and Learning framework at Queen Mary University of London, specifically to guide educators as they embed relevant activities into their teaching. Our model is inspired by the AI literacy framework introduced by Davy Ng, Samuel Chu, and Maggie Qiao of the University of Hong Kong and Jac Leung of the Hong Kong University of Science and Technology.

Their pedagogical approach consists of four dimensions:

Know and understand AI. Students first build a solid foundation of knowledge about generative AI (GenAI), so that they understand the fundamental concepts, capabilities, and limitations of GenAI technologies.

Use and apply AI. Next, they work on cultivating practical skills, utilizing GenAI tools in various learning contexts.

Evaluate and create with AI. Finally, students are encouraged to engage in critical thinking and creativity. They are pushed to assess the quality of AI applications, evaluate the content they generate, and innovate using AI technologies. 

AI ethics. They are asked to consider AI’s ethical and responsible use through all the dimensions above.

Similarly, the goal of our own framework, described below, is to ensure that all students progressively develop their AI skills throughout their studies.

A Quick Introduction

Our model includes three levels of progression. At the foundational level, students undertake learning activities that allow them to understand basic AI usage for tasks such as searching for information, checking grammar, and applying ethics when using AI for academic writing. (This level coincides with the “know and understand” dimension.)

At the intermediate level, students use and apply AI technologies for higher-order thinking and learning activities such as analysis and problem-solving (“use and apply”). They reach the advanced level when they can use AI tools for creativity, innovation, critical analysis, and co-creation (“evaluate and create”).

These levels of progression—from lower-order thinking to higher-order thinking—reflect the activities and educational goals depicted in Bloom’s taxonomy.

A Bottom-Up Approach to AI Integration

We recognize that not all educators are skilled with AI, nor do all educators feel confident incorporating AI into their teaching or allowing students to use the technology for learning support. That’s why we recommend taking an incremental bottom-up approach to implementing the framework.

Faculty who teach in Queen Mary’s BSc Business Management program ease AI into their teaching, slowly integrating it into extracurricular and core-curricular activities. At the extracurricular level, all students are invited to enroll in a self-directed learning program that covers various aspects of AI literacy, such as using AI for literature searches, academic writing, and revision. Students who successfully complete the program receive certificates that recognize their acquired AI skills and knowledge.

We also offer several optional face-to-face training opportunities where students learn to use AI to support their learning. These sessions explore topics relevant to the technology such as data analysis, ethics, and creativity.

Activities to Develop Each Level of Literacy:

Although extracurricular activities can be an integral way for students to build their AI literacy, they also must have deliberate, ongoing exposure to the technology across one or more dimension. That’s why, in our core undergraduate curriculum, we integrate AI into at least one compulsory module in each of the program’s three educational levels:

• At Level 4 (Year 1), AI is incorporated into Challenges in Business and Management, a module that develops foundational AI skills. Students use GenAI tools for learning activities such as notetaking, presentations, and lecture summaries. At this stage, faculty teach students how to use GenAI ethically for presentations and how to acknowledge that use.
• At Level 5 (Year 2), AI is incorporated into Working with Business Data, a module that develops AI skills at both the intermediate and advanced levels. Students use AI tools such as Copilot, Power BI, Bricks, and Julius for data analysis and interpretation. Faculty address AI ethics by asking students to analyze AI-generated content to detect bias and false information.
• At Level 6 (Year 3), AI teaching and learning activities are embedded in Strategic Analysis and Practice, an advanced-level module that focuses on higher-order thinking and application. Students co-create content with AI as part of class simulation activities, while considering the ethical and responsible use of AI for additional learning support.

We not only encourage students to use free GenAI tools, but also offer access to the web-based version of Copilot that is licensed by the university. This ensures that our AI curriculum is inclusive and makes it far more likely that all students will acquire essential AI skills.

Additionally, Queen Mary’s School of Business and Management supports its staff’s AI pedagogical knowledge. For example, each year the school holds four sessions where faculty can share best practices, as well as highlight the ways they use different AI tools for teaching and learning, assessment and feedback, and research.

Practical Suggestions

When schools design and adopt new AI curricula, program directors play a critical role in mapping program learning outcomes, identifying gaps in students’ AI skills, and finding ways to close these gaps by introducing new modules or extracurricular activities. We offer the suggestions below for program directors who wish to adopt the AI Teaching and Learning framework in their programs:

• Integrate AI skills across the curriculum. Don’t treat AI as a standalone subject. When faculty make AI part of every subject, students will see its relevance in different contexts and be better prepared for its widespread application in the workplace.
• Incorporate AI at a module level. This will ensure that all students engage with the tools in active learning. For example, to address the “use and apply” dimension, Queen Mary incorporated AI into its capstone project module. More than 90 percent of students have reported satisfaction and engagement with the activities.
• Incorporate AI into at least 10 percent of a program’s content. By meeting that minimum target, schools make it more likely that at least one module at each level will provide students with opportunities to develop AI skills.
• Support AI literacy with extracurricular activities. Extracurricular offerings can include self-directed AI literacy training modules that cover topics such as using AI for learning, information searches, writing, and revision.

Advantages to a Bottom-Up Strategy

Business schools face greater urgency to develop students’ AI literacy skills through rapid adoption of AI in their classrooms. A top-down approach often involves cumbersome approval processes that delay action. Conversely, bottom-up approaches face less resistance from educators, because AI integration for teaching and learning is not imposed on them but championed by them.

Indeed, bottom-up strategies are critical when it comes to faculty effectively using AI’s potential to enhance students’ AI literacy. When module organizers and tutors take the lead, they not only will tailor AI integration to suit educational goals, but also will advocate its benefits to peers, creating a supportive network that accelerates adoption.

The educators who are closest to students’ learning processes are the ones who are best able to integrate AI into teaching and learning. That is why it is so important for business schools to provide students with a clear framework to help them develop their AI literacy. With such support, these educators will ensure that students remain employable and empowered in the AI-enabled economy.

To gain access to Queen Mary’s AI in Teaching and Learning framework, please complete this short form.