The PAIR framework for using GenAI in education

Professor Oguz A Acar, Professor of Marketing & Innovation

In the previous step, you discussed how teachers are using generative AI tools. In this step, you will discuss a framework that is designed to proactively integrate AI into our curriculums.

Generative AI is poised to create transformative changes, impacting not only the educational sector but also the wider professional world. In the face of these advancements, a purely defensive approach, centred on detecting and reducing AI usage, does not sufficiently equip our students for an AI-driven future.

The PAIR (Problem, AI, Interaction, Reflection) framework emerges from a motivation to proactively integrate AI into our curriculums. Specifically, it aims to cultivate five essential skills that are paramount for leveraging these technologies responsibly: problem formulation, exploration, experimentation, critical thinking and reflection (for a deeper insight into the importance of these skills, and details of the framework, please refer to my article, Are your students ready for AI?. The ultimate objective is to ensure that students can utilise generative AI tools effectively and ethically.

Key components of the framework

I developed the PAIR framework with simplicity, customisability and compatibility in mind, allowing it to be seamlessly adapted across various disciplines. The three key tenets of the framework are:

• Human-centric: viewing AI as a tool to augment, not replace, human insight, judgment and creativity.
• Skill-centric: focusing on the development of transferable AI skills over mastering specific tools.
• Responsibility-centric: promoting effective and responsible use of generative AI tools.

The PAIR process is encapsulated in four steps:

• Problem formulation: students define the problem or challenge they aim to address.​
• AI tool selection: students identify and choose the most suitable generative AI tools for their needs. This involves exploring, comparing and evaluating the features of various generative AI options.
• Interaction: students deploy the selected generative AI tools to tackle their problem, experimenting with diverse inputs and critically evaluating the outputs.
• Reflection: students assess and report their experiences with the generative AI tools.

The table below provides a practical guide for applying the PAIR framework:

Implementing the PAIR framework

Below are some ways that PAIR can be tailored to suit different academic objectives and contexts.

The PAIR framework can be seamlessly woven into existing coursework or serve as a foundation for the design of a new module. For the former application, module leaders could simply invite students to tackle the whole task or challenge highlighted in the assignment, or parts of it, using the PAIR framework. It is worth noting that ‘problem’ is broadly interpreted as a task or challenge integral to the assignment. This encompasses, but is not limited to, diverse tasks such as analysis or comparison (for instance, of legislation, data, code, case studies, theories, movements or ecosystems) and creation or design tasks (like developing algorithms, artworks, models, business plans, research protocols, simulations or chemical compounds).

Each component within the framework can be adjusted based on learning goals, available resources and the specific needs and attributes of the student body to ensure the framework complements the learning environment and supports the achievement of learning objectives.

1. Problem formulation

This is the foundation of PAIR. Approaches to this can vary, such as:

• Open inquiry: students are granted autonomy to identify and craft their problem statement.
• Closed inquiry: a structured format where students are given a predefined problem.
• Semi-open/guided inquiries: module leaders can offer broad themes or ill-defined problems, letting students focus on specifics.

2. AI tool selection

This can also be tailored per the module’s needs. Module leaders might, for example, suggest a list of freely accessible foundational models, such as Bard, Bing AI, ChatGPT 3.5, or Claude 2, in alignment with the learning objectives. In addition, or alternatively, students might be encouraged to explore and select AI tools based on their research. If the second option is preferred, it is important to ensure all students can access the recommended AI tools and recommend them to solely rely on free tools (or use those offering free trial versions).

3. Interaction

This stage encourages students to actively engage with their chosen AI tools. Emphasis can be placed on experimenting with different techniques to understand the tools’ capabilities, or on the critical evaluation of results. Depending on learning objectives, module leaders may guide students to delve deeper into understanding advanced prompting strategies, feedback mechanisms and the nuances of AI responses. Alternatively, the focus might shift to scrutinising the quality, potential biases and underlying logic behind AI-generated outputs.

4. Reflection

This phase encourages students to introspectively analyse their experiences with the AI tools. Module leaders can provide avenues for students to contemplate the broader implications and intricacies of AI outputs. Topics of reflection might encompass AI’s societal effects, its influence on specific disciplines, professions or tasks as well as their identity. Aligning these reflective prompts with course objectives encourages students to better contextualise their learnings and the potential ramifications of AI in the wider world.

In adapting the PAIR framework to coursework, it is important to tailor the complexity of the assignment (eg whether an inquiry is open versus closed) based on student characteristics such as their prior knowledge and cognitive abilities. It is also essential for module leaders to develop an understanding of generative AI tools.

Now that you have completed this step, you have developed an understanding of how you can proactively integrate generative AI in your module design. In the next step, you will learn about chatbots and study assistants.

References

1. Acar OA. Are Your Students Ready for AI?: A Four-Step Framework to Prepare Learners for a ChatGPT World [Internet]. Harvard Business Publishing Education; 2023 Jun 15.

Join the conversation

Considering the ‘Reflection’ component of the PAIR framework, how essential do you deem the process of introspection and contemplation after engaging with generative AI tools? Can taking a step back and reflecting alter the way we understand and harness AI? Share a specific moment or learning experience where reflection reshaped your perspective on AI or its applications.