This activity will explore the current landscape of AI-assisted education by analysing existing methodologies, research trends, and best practices. A key focus will be integrating Bloom’s taxonomy into a Retrieval-Augmented Generation (RAG)-based Large Language Model (LLM). The research will identify gaps and challenges in AI-powered education, providing a foundation for developing structured, pedagogically sound AI-assisted learning tools. 

This activity will assess the ethical risks associated with AI-powered education, including issues of bias, transparency, academic integrity, data protection, and responsible AI use. The Activity will result in reports that analyse how AI systems shape knowledge production and learning outcomes. We will produce actionable guidelines for responsible adoption of BloomingAI, to ensure that BloomingAI aligns with GDPR and other regulatory frameworks and academic integrity policies, addressing concerns about fairness, accountability, and inclusivity.

This activity focuses on developing AI-powered teaching, learning, and assessment assistants that align with the Bloom taxonomy’s six cognitive processes (remembering, understanding, applying, analysing, evaluating, and creating) and four types of knowledge (factual, conceptual, procedural, and metacognitive). Our aim is that AI assistants will support structured student engagement, personalized learning, and adaptive feedback. This phase includes testing different AI models and refining their pedagogical application. 

This activity will develop a structured methodology for integrating BloomingAI into university curricula and assessment frameworks. Educators will receive practical guidelines on incorporating BloomingAI into their teaching strategies while maintaining academic rigor. The methodology will address AI’s role in grading, assessments, and academic integrity, ensuring a human-in-the-loop approach where necessary.