Artificial intelligence (AI) has been dominating the conversation in academic spheres with very little concrete information or practical solutions available for the issues generative artificial intelligence raises in academia and research. AI has become a buzzword that can attract a big crowd to a webinar, but with the amount of raw information, speculation, reports of trials, and so much more available out in the world, it can be very difficult to pin down what exactly the emergence of these new, more sophisticated AI models and tools means for Higher Education institutions, and what academic and professional staff should do or learn about AI. What is clear, however, is that these tools are here to stay – and students are already using them, whether we like it or not.
AI, machine learning, and natural language processing are all concepts within a fast-moving industry, which often means that any guidance produced for making the most of AI models or acknowledging their limitations is out of date almost as soon as it’s published. But the principles for using AI models and tools ethically are changing much slower and are more closely related to the discussions you might need to have with students about AI.
The most important thing we can do for students is provide them with clear and consistent advice on the use of AI in their academic work.
Further exploration of this theme and examples can be found on our Quick Guide in Moodle.
Student guidance: Artificial intelligence guidance
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