Read widely, think rigorously, evaluate critically.
Rapid advancements in AI capabilities raise a major question for young people today: why bother learning anything if AI can do my schoolwork for me?
Students with this mindset become dependent on AI tools to perform tasks that were considered non-negotiable just a few years ago. Many are reading summaries instead of books, generating automated essays from bullet points rather than writing fluently, relying on translation software rather than learning languages, and bypassing other challenging, rewarding, and essential aspects of learning. This weakens discipline, concentration, and mental flexibility.
AI tools may perform certain academic tasks faster and more accurately than we can, but they cannot inspire the independence, dignity, or confidence that arise from real problem-solving and analytical thinking.
Human intelligence isn’t obsolete in an AI‑powered world; the thresholds for what constitutes it have merely risen. Anyone capable of independent reasoning, debating, and forming original judgements is inherently better equipped to face the future of education and work.
Reading must never be outsourced to AI. Relying on summaries in place of original texts sacrifices our genuine intellectual understanding and has serious cognitive implications.
Reading deeply and widely forces us to persevere through difficult passages, make connections between ideas, form original interpretations, and develop many other crucial skills. In doing so, we strengthen our attention span, memory, reasoning, creativity, and metacognitive awareness. These skills are all essential to our neurological health, academic performance, and professional pursuits.
Our tutors pursued PhD research because they read widely and fell in love with the ideas they encountered. We hope to inspire our students to find the same passion and purpose in their learning.
We understand that AI is here to stay and can be leveraged constructively. We coach our students to treat AI tools as competent research assistants that can produce revision schedules, generate practice exam questions, organise notes, and perform other tasks that elevate their productivity.
If students use AI to generate feedback on their work, they should do so interactively, rather than blindly accepting an LLM’s outputs. This means prompting models to provide explanations of errors, suggestions for improvement, and probing questions, rather than simply ghostwriting alternative answers. The key is for AI to act as a highly intelligent critic that pushes us to think more rigorously and critically about our work.
We are equally determined to prepare our students for the future of learning, academic research, and work in an AI‑powered world. In our tutorials, we consider ethical and methodological questions surrounding AI’s applications across different fields.
Could algorithmic analysis of social media data redefine sociological studies on public opinion? Is translation software rendering linguists redundant? How do debates surrounding AI’s consciousness inform philosophical enquiry? How could AI‑generated art complicate long-standing debates in aesthetics about authorship and originality?
Engaging with these questions gives students the perspective and preparedness to approach an AI‑powered future with their human intelligence and critical thinking skills intact.