AI Accelerator Day – UK Language Courses

Client: UKLC

Event: AI Accelerator

Industry: Education

Date: 25th March 2026

Consultant: Rufus Cunrnow

AI Literacy in Practice: Building Foundations for Generative AI in Education

Executive Summary Dixon AI delivered an AI Accelerator Day for UK Language Courses (UKLC) in Liverpool, designed to build foundational understanding of generative AI across a mixed-ability group. The session introduced core concepts, practical tools and hands-on experimentation, enabling participants to move from initial curiosity to applied understanding. Positioned within Stage 3 of the AI Transformation Playbook, the event established a shared baseline of AI literacy and created momentum for continued learning and application.

Business Context As an education provider, UKLC operates in an environment where content creation, communication and learning design are central to daily work. The emergence of generative AI presents both an opportunity and a challenge: staff must understand how to use these tools effectively while maintaining educational quality and integrity.

This event aligned with Stage 3 of the AI Transformation Playbook, where organisations begin building organisational AI literacy. At this stage, the priority is developing a shared understanding of how AI works, what it can do and where it fits within existing workflows.

Objectives of the Event

• Build a clear understanding of generative AI and related technologies

• Introduce practical prompting techniques and reusable structures

• Explore the differences between generative and retrieval-based AI

• Provide hands-on experience with AI tools, models and agents

• Encourage early-stage experimentation within an educational context
  

What Happened During the Event The session was delivered as a full-day, hands-on workshop with a strong emphasis on participation and practical exploration.

Participants were guided through the fundamentals of generative AI, including how models operate and how they differ from retrieval-based systems. This created a shared conceptual baseline across the group, regardless of prior experience.

From there, the focus moved into prompting. Attendees worked through structured approaches to writing prompts, then progressed to building reusable prompts that could be applied in their own roles. This shifted the interaction from one-off use to repeatable workflows.

The session then expanded into tools and applications. Participants explored a range of models and platforms, including the creation of custom bots and agents such as GPTs, Gemini Gems and Copilot Agent assistants. This allowed them to see how AI can be shaped around specific tasks rather than used in isolation.

Later in the day, the group experimented with basic “vibe coding” exercises, creating simple language-learning applications. This provided a practical demonstration of how AI can support educational product development, even at an early stage of capability.

The session concluded with a discussion of ethics and responsible use, ensuring participants considered not just what AI can do, but how it should be applied within an educational setting.

Key Insights and Takeaways The session reinforced that early AI adoption is less about technical depth and more about building confidence through use. Once participants understood how to structure prompts and interact with tools, their ability to experiment increased quickly.

The distinction between generative and retrieval AI proved important. Clarifying this helped participants better understand when to use each approach, particularly in contexts where accuracy and source grounding matter.

Reusable prompts emerged as a practical bridge between learning and application. Rather than starting from scratch each time, participants began to see how structured inputs could improve consistency and efficiency in their work.

The introduction of agents and custom tools shifted thinking further. Instead of viewing AI as a single interface, participants began to consider how tailored tools could support specific tasks or workflows within education.

The mixed ability of the group did not slow progress. In practice, it created a collaborative learning environment where participants could share insights and learn from different perspectives.

Impact By the end of the session, participants had moved beyond basic awareness into practical understanding. They were able to use AI tools with greater confidence, structure their interactions more effectively and recognise where these tools could support their work.

The event established a shared language across the group, reducing uncertainty and enabling more informed discussions about AI going forward. It also created visible momentum, with participants taking initial steps in their own AI learning journeys.

Importantly, the session reframed AI from an abstract concept into something tangible and usable within an educational context.

What Happens Next Following Stage 3, the next step is to translate literacy into application. This typically involves structured experimentation, where participants begin applying AI to real workflows and identifying opportunities for value creation.

For UKLC, this would involve progressing into Stage 4 of the AI Transformation Playbook, where individuals and teams begin building reusable tools, testing ideas and developing early use cases aligned to their roles.

Closing Insight AI capability in education does not begin with complex systems. It begins with individuals understanding how to use the tools in front of them. When that understanding is shared across a group, it creates the conditions for meaningful change in how learning, content and communication are delivered.

Organisational AI literacy and applied AI training remain the foundation for effective AI transformation.