With a goal to elevate A-level students' grasp of complex physics topics, a lesson was crafted to intertwine AI's generative capabilities with traditional teaching. At the heart of this lesson was the objective to improve students' written responses to explanatory questions.
Students initiated their interaction with a sample prompt, clearly outlining their needs and the specifics of their study material. By incorporating relevant sections from their course specification - areas where they'd previously shown weaknesses - the AI was tasked with generating pertinent questions. These weren't just any questions; they were tailored, focused, and targeted to hone students' explanatory ability.
The Sample Prompt: 'I am a year 12 student of A level Physics in the UK. I am revising the topic of X, and am struggling with coherently explaining Phenomenon Y. If I give you the relevant sections from the specification I am studying, can you ask me questions that require explanations only on Phenomenon Y. I will then provide a written explanation myself. I would then like you to evaluate the strengths and weaknesses of my explanations and guide me using a Socratic questioning method towards improving my written responses. Before we begin, ask me any questions you need to fully understand what I'd like us to do.'
Teachers, in this AI-enhanced setting, took on supervisory roles. They navigated the classroom, ensuring optimal AI use, analysing the prompts students engaged with, and observing the AI's Socratic dialogue unfold. This wasn't just a lesson on physics; it was an exemplar on how AI can be a powerful ally in personalized learning. Within this framework, students were not passive learners. They actively discerned the utility of AI, understanding how to leverage it for their academic betterment.
The outcome? An AI tool that was not only efficient in crafting explanatory questions but also a significant asset that eased teachers from the burden of extensive preparatory work.
The scenario in this case study is genuine and based upon real events and data, however its narration has been crafted by AI to uphold a standardised and clear format for readers.
Generative AI can drastically enhance targeted learning. Tailored questions based on specific student needs can bridge knowledge gaps. Teachers can transition from traditional roles to supervisors of AI-enabled personalised learning, ensuring quality education. This works very well as an introductory exercise in AI use to aid independent teaching and learning. It can also allow for a significant amount of teacher input; following an assessment or homework each student can be given a target explanation to improve and this can serve as a shorter lesson exercise once students understand better how to use the AI effectively.
AI can improvise and add content that is not required in certain exam board specifications, particularly relevant in the sciences where subtle differences can exist across similar content. Encourage the students to always refer back to the specification if they think they are being asked something that their board does not require, or seek guidance from the teacher. Generative AI has a sketchy relationship with maths, and asking it for sample calculation problems, for example, can lead to it making mistakes that students might not notice. Over reliance on AI can diminish critical thinking. There's a need to ensure AI complements, not replaces, human-guided learning. Without proper oversight, there's a potential for misunderstanding or misinterpretation of AI-generated content.