Strengths and Limitations of Generative AI

Considerations when using generative AI

The University of Lincoln is currently exploring the opportunities and challenges that could potentially arise from using generative AI within teaching and learning. As stated in our policy section, the decision to use AI is being left to local areas to see if it’s suitable for their own contexts. However, it is important to think of the potential strengths and limitations of using generative AI when considering embedding it with in your teaching.

Strengths

Automated Content Generation:

Generative AI can produce a wide range of content, including but not limited to images, text, and media, based on specific requirements.

Ideation

AI can be used to help support development of ideas and construction of content.

Data analysis

Although we need to be careful with the data we input, AI can help analyse data to provide insights into patterns and areas it discovers.

Summarisations

AI can successfully summarise and feedback information in easy to digest information.

Limitations

Quality and accuracy

AI can create content quickly, but the quality and accuracy of the data is only as good as the information the AI uses. It can also produce ‘false data’ (also known as hallucinations) so that it can find an answer to the prompt used. The user will need to check the information before use.

Ethical concerns

There are always ethical concerns about the use of AI, especially when it relates to academic integrity. The way the tool is used within teaching and learning needs to be considered from this standpoint.

Bias and fairness

AI models only learn from the models that they have available, meaning that existing biases could be perpetuated when returning results. Identifying the data sources and thinking about the wider implications and impact of that data is essential.

Potential for low quality responses

Each response is only as good as the initial prompt. Writing prompts need to be considered carefully to ensure that the data returned is suitable for purpose and meets requirements.

Declaring sources

Whilst some AI tools do specify where sources have come from, these sources still need to be checked and verified as AI tools can create false data sources to make data look more convincing. It should also be clarified that some AI will provide sources which can also be falsified.

Contextual understanding

Whilst AI models have developed to enable them to look at context in wider capacities, it needs to be acknowledged that the extent and detail to which AI can understand context is still limited and may require human intervention.