Educators face new challenges in identifying artificial intelligence-generated work among students, as recent guidance from the Joint Council for Qualifications (JCQ) highlights specific language patterns often associated with AI writing tools. The JCQ’s list of indicative words and phrases aims to help teachers detect potential use of AI in student assignments. However, some education professionals caution that such measures may unfairly target pupils who have been trained to write thoughtfully and precisely.

Paul Morgan, head of St Julian’s School in Lisbon, expressed concerns that the checklist approach could inadvertently penalize careful student writing. He noted that AI models typically generate text that reflects well-constructed and measured prose, whereas a student who deliberately selects exact wording to convey nuanced meaning might appear suspicious. Conversely, a student instructing an AI to "write like a 16-year-old" may produce work that escapes detection by these linguistic markers.

Dr. Thomas Lancaster, an expert referenced by Morgan, advocates for a more interactive strategy to address the issue. He emphasizes the value of oral dialogue, suggesting that teachers engaging students in explaining their written work aloud can more effectively reveal whether the ideas were genuinely composed by the pupil. This approach, however, requires substantial time investment from educators.

Morgan underscored that no list of flagged words can substitute for the thorough understanding and interaction between teacher and student necessary to ensure academic integrity. As AI tools become increasingly sophisticated and accessible, educators must balance the need for detection with supporting students’ development of critical writing skills.