Four steps for integrating generative AI in learning and teaching

Generative AI is a powerful tool that can be used to facilitate deep learning. The key challenge for universities, in response, however, has been how to adapt to the ways in which students are using AI tools and to address the potential risks these pose to academic integrity.

Here, we offer four steps as a path towards engaging AI tools in teaching and learning. This checklist emphasises higher-order-thinking tasks, such as evaluation and analysis.

1. Use AI tools to promote inclusive, interactive, personalised learning

Since AI tools incorporate natural language processing, this facilitates a more intuitive and engaging interactive learning experience for learners than is available through asynchronous learning materials such as recorded lectures. This makes AI tools a useful resource for teachers, who then have more time to develop an active and interactive classroom dynamic.

• Collection: AI transformers like ChatGPT are here, so what next?
• Students see the benefits of AI-generated learning content
• Embrace AI tools to improve student writing

For example, students could use generative AI to suggest public health interventions that improve nutritional status among pregnant women. They could then modify the prompts (such as participant selection criteria, recruitment strategy, intervention strategy and data analysis) to make the interventions more inclusive, culturally sensitive and sustainable in their communities.

Generative AI is also useful for reinforcing information through repetitive interactions, which aid comprehension and retention. Students can engage with AI tools in repeated conversations on the same topic, for instance.

The final learning opportunity in this exercise would be for students to explain how their answers differed from the AI-produced response.

As these assessment tasks are more personalised and contextualised, they allow students to demonstrate their intellectual independence and understanding.

2. Evaluate the hierarchy of evidence and accuracy of AI-generated content

AI-generated content is not always accurate. It also reflects the social biases and inequalities of the data used to train it, including certain limitations in languages other than English. However, these limitations can be exploited to foster students’ critical-thinking and analytical skills. Misinformation or bias in AI content can be used to raise learners’ awareness of the limits of these tools and the dangers of over-reliance on them.

For example, during teaching exercises, students could be asked to critically analyse an AI-generated public health policy. Teachers can guide students to identify the gaps where more detail is needed, pinpoint bias, evaluate the strengths and weaknesses of the policy content created, and examine whether the texts are written with appropriate tone, diction, style and voice.

Teachers need to emphasise the importance of proper citations and teach students how to cite reference sources correctly as this relates to academic integrity. In public health research, the decisions to implement interventions and policies are made based on the evidence, which directly affects the health and safety of communities. Students could be asked to use reliable sources (such as articles published in peer-reviewed academic journals) to verify the AI-generated content. Students could also evaluate the accuracy and reliability of the Al-generated content by looking for inconsistency or contradictions within the text generated by AI tools.

If students are taught about the acceptable use of AI tools in their learning and preparation for assessment, this helps them to understand the demands of academic integrity and to avoid academic misconduct.

3. Propose alternative approaches for problem-solving beyond AI responses

Another approach is to create and analyse counter-arguments. Students provide as much information as possible to the generative AI on a controversial public health topic. Then the generative AI is asked to provide counter-arguments or alternative scenarios.

This teaching approach encourages students to use their judgement to engage with and analyse the information, debate the issues and make real-world decisions. In addition, students’ reasoning with peers and teachers can provide evidence of their critical-thinking skills in their course assessments.

The focus of teachers should be on how students reveal their decision-making processes and respond to counter-arguments rather than on how they use AI tools to generate the responses.

4. Use what, how and why prompts to reflect on learning

Students should be asked to reflect on how they use AI tools to improve their learning experiences. For example, students who have no prior knowledge of the topic (that is, the “What?” question) may use AI to prepare before coming to the class. After the lesson, students tailor questions and prompts to better understand the concepts (“How?”) and make sense of the knowledge learned (“Why?”). Students are then asked to reflect on what worked well and what did not in their use of AI tools to improve their learning experiences using a closed-loop reflective cycle approach.

Students could be asked to share their ideas on how to use these tools in other learning contexts as part of a classroom discussion. For example, students could provide advice to peers who are struggling with the same concepts. Students could also reflect on how easy or difficult it is for them to identify work produced by generative AI and what makes them think it is produced by generative AI.

AI tools will evolve over time, so we should adapt our teaching as they develop. We should be consistent, though, in our focus on supporting students to develop higher-order-thinking tasks, such as evaluation and analysis, through these tools.

Zheng Feei Ma is a senior lecturer in public health and Antony Hill is dean of learning and teaching, both in the College of Health, Science and Society at the University of the West of England (UWE Bristol).

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