TPACK framework and AI in genomics training

It is worth emphasising that when we talk about Artificial Intelligence (AI) in this course, the focus is on its use in genomics and bioinformatics teaching and training. We still need to bear in mind that AI/ML is also widely used in genomics research and practice.

In teaching/training, generative AI tools such as ChatGPT or Copilot can help us design, develop and evaluate learning more efficiently – by drafting materials, suggesting activities, or supporting reflective feedback.

In genomics research and practice, machine learning and AI methods are widely used to analyse data – for example for detecting patterns in genomes, predicting phenotypes, or supporting diagnosis.

It is important to make this distinction clear in terms of what our focus here is. The first enhances how we teach, the other advances and supports what we teach. The course focuses mainly on the teaching/training aspect.

As technology changes constantly, and AI in particular is a moving target, our approach to adopting new technology in training should be as always strategic, sustainable and coherent, i.e. aligned with appropriate pedagogy and training goals.

Considering AI separately from the rest of the technology already adopted and embedded in teaching and training, is not desirable for several reasons. For example, treating it as a standalone phenomenon, risks creating artificial boundaries between AI-driven tools and the rest of the digital learning ecosystem. What we need in practice is technology-enhanced education based on these tools working together, under human oversight. We are witnessing the fact that many tools we are using, such as those developed by Google or Microsoft, now already have the AI tools embedded within them. So, we should be looking to use educational technology to enhance the training and learning, and not to showcase technology itself, however impressive it is.

If educational institutions already have frameworks and governance structures for technology adoption, AI should be categorised within these frameworks to avoid duplicated efforts caused by creating a separate category.

By embedding AI (and we are focusing in this course mainly on GenAI) within the broader digital learning context, trainers can focus on the outcomes it enables (for example personalised learning, faster feedback, better engagement) rather than on the tool as a trend.

TPACK framework applied to genomics

We talked in an earlier step, step 1.13, about the TPACK model for the inclusion of technology to enhance learning and training. When Large Language Models (LLM) appeared as relevant tools for use in education, the research on integrating GenAI into education through the TPACK model has gained significant attention.

Research is highlighting the need for educators to develop specific competencies in order to adopt AI tools and is calling for more comprehensive frameworks to guide educators in adopting AI technologies in their teaching practices. ​

To look at the elements of TPACK model, as applied to genomics/bioinformatics and with references to AI click the link below to unpack them one by one. You can then leave any comments or questions that you might have in the comments section below.

TPACK model applied to genomics/bioinformatics

Please find a PDF with the link content provided at the end of this step.