October 2026 Get Online London Meet-up: AI Literacy

We opened the session with reflections on the key findings from our previous Get Online London AI meet-up back in October 2025 and brought things up to date with new insights gained from recent workshop activities in London over the Summer.

Within breakout rooms, we focussed on current AI tools and resources being used and asked what the key challenges are right now. We also asked how London hubs feel AI is impacting the employability landscape.

Here is what we heard:

Tools and uses

• Core tools: Copilot, Gemini, ChatGPT (free version), Google’s AI suite, and Teachable Machine (used for demonstrating AI algorithms to children).
• Workplace and administrative efficiency: Copilot and Gemini are widely used for mapping project tech requirements, reviewing policy documents, conducting workplace research within Google Drive, and generating reports or presentation slides.
• Direct service delivery and note-taking: Copilot and specialised tools like Beam automate meeting notes and populate care-visit forms in real time, freeing up staff time for direct client care.
• Teaching and creative tasks: AI is being leveraged to create teaching materials, build website components (e.g. carousels), and draft email communications.

Challenges and barriers

• Data security and privacy: Significant reluctance and concern around inputting sensitive personal, medical, or financial information, along with worries over excessive app permissions.
• Trust and reliability: Widespread scepticism about output accuracy, hallucinations, and the risk of unverified AI-generated content.
• Knowledge and prompting gaps: Lack of clear understanding regarding effective prompt engineering and what "AI literacy" means in daily practice.
• Staff and community adoption: Steep learning curves for staff, low trust among learners, and a lack of accessible AI training specifically tailored for volunteers.
• Cost and accessibility constraints: High subscription fees for advanced AI tools (e.g. specialised note-taking tools or paid tier search features) risk excluding the charity sector and widening equity gaps.

Employability impacts

• Low confidence and vague demands around AI literacy: Participants reported low confidence in understanding employer needs, noting that job descriptions rarely specify explicit AI skills in the way traditional software (like Excel) is listed.
• Unclear standards: Workplaces and employers are still defining what AI literacy entails, leading to vague expectations around how candidates should apply AI to specific roles.

• Application and interview assistance: AI is actively used to discover job search strategies, refine CVs, match applications to role profiles, and generate practice interview questions.
• Emerging digital divide: A growing inequality exists between those who can afford paid AI tools or possess advanced prompting skills, and those who lack access or skills.
• Application quality and detection risks: Copy-pasting AI output can mask genuine skills or backfire if leftover prompts are accidentally included, severely undermining candidate trust.
• Assessment challenges for hiring managers: Highly polished, AI-generated applications make it difficult for employers to accurately evaluate a candidate's true English literacy or job aptitude.
• Job security and real-time misuse: Concerns were raised regarding job displacement in entry-level/data-entry roles, as well as applicants inappropriately attempting to use AI during live interview assessments.

Good Things Foundation has a range of free resources to support you in building AI literacy within your community. Take a look at our AI Gateway for basic AI skills. For more resources and more advanced learning content view our AI collection.