Gen AI at a glance

Learnovate’s suite of short research reports explores how generative AI is reshaping learning, teaching, and assessment across a range of educational contexts.

One major focus is on how generative AI will transform teachers’ planning and assessment practices, highlighting both opportunities and challenges. The project notes that teachers already struggle with time, resource constraints, and a rapidly expanding technology landscape. Generative AI offers new possibilities, such as automating subtasks, enriching formative assessments, and creating more diverse outputs, but effective use requires thoughtful lesson design and an understanding of AI’s implications for learners, teachers, institutions, and society.

Another Learnovate project examines the user onboarding experience for generative AI tools, particularly for non‑technical users. This work identifies the challenges users face when learning how to interact effectively with AI and outlines best practices for designing onboarding processes that improve engagement and satisfaction. It emphasizes that onboarding must help users understand “what they can do and how to do it” with generative AI, making adoption smoother across sectors including education, media, research, and customer service.

A further research stream explores generative AI and metacognition, investigating how AI can support learners in becoming more aware of their own thinking processes. Metacognition—planning, monitoring, and evaluating one’s learning—is strongly linked with better learning outcomes. The project describes how generative AI can serve as a mentor, offer personalized content, provide learning nudges, guide reflection, and even deliver assessments that strengthen metacognitive skills. While showcasing these opportunities, the report also cautions against potential downsides and emphasizes responsible design and implementation.

Learnovate also examines generative AI for authentic point‑of‑need learning, questioning whether AI simply speeds up access to information or can truly create more valuable, personalised, and trustworthy learning experiences. The report highlights concerns about superficial chatbot-style interactions, lack of personalization, and risks associated with retrieving information from external sources in regulated corporate environments. It emphasizes the need for AI systems that build authentic relationships with learners, integrate organizational content safely, and elevate point‑of‑need learning beyond quick answers.

Finally, the project “Exploring Generative AI and the Future of Learning Technology” situates these topics within a broader landscape. It discusses the rapid rise of generative AI, its availability to the general public, and the surge of experimentation across industries. This work introduces Learnovate’s series of short reports (including the four above) and frames generative AI as a powerful driver of educational innovation. It highlights emerging use cases, potential disruptions, and the need for educators and organizations to understand both the benefits and limitations of generative AI as it becomes increasingly embedded in learning technologies.

Learnovate’s Moving Towards More Authentic Learning Experiences series extends this work by looking at how generative AI can deepen authenticity in real‑world, practice‑aligned learning contexts. One report focuses on using generative AI beyond performance support, exploring its potential as a learning tool for software developers, an area rapidly evolving due to AI‑assisted code generation. Another report investigates using generative AI for Learning Needs Analysis, examining whether AI can streamline the process of identifying learner requirements. Given that needs analysis is essential but often time‑consuming, this work considers AI’s potential to make the process faster, more comprehensive, and more robust.

Together, these reports provide an integrated picture: generative AI can enable more efficient, personalized, reflective, and authentic learning experiences, but only when carefully applied with attention to context, pedagogy, and data integrity.