# data[].attributes.headline
Each group writes a shared definition/outline of the area to improve on Padlet.

# data[].attributes.subject
Each group writes a shared definition/outline of the area to improve on Padlet.

# data[].attributes.headline
What specific area can you improve with AI from your previous discussion?

# data[].attributes.subject
What specific area can you improve with AI from your previous discussion?

# data[].attributes.body
Prompts:

What does this look like in our classrooms?

Why does it matter for our learners?

Why do we want to improve it?

# data[].attributes.body
Name Steve Wileman, Alan Handy , Steve Byrne

________________________________________

Strategy to Trial in Delivery:

Using AI to generate personalised feedback for learners based on their written work and responses in class.

________________________________________

How Will You Know the Strategy Has Been Successful?

• Learners receive feedback more quickly.

• Feedback is more specific and actionable.

• Tutors report reduced workload and improved efficiency.

________________________________________

Expected Impact:

• Improved learner engagement and confidence.

• Greater consistency in feedback across departments.

• Enhanced support for individual learning needs.

# data[].attributes.body
xxxx

Shared Definition / Outline:

xxxx

What does this look like in our classrooms?

xxxxx

Why does it matter for our learners?

xxx

.Why do we want to improve it?

xxxx

Group Members:

xxxx

# data[].attributes.headline
All staff to complete the following on the Padlet

# data[].attributes.subject
All staff to complete the following on the Padlet

# data[].attributes.body
Name

What strategy are you are going to trial in your delivery

How will you know the strategy has been successful?

What impact do you expect to see?

# data[].attributes.body
xxxxxx

________________________________________

Strategy to Trial in Delivery:

xxxxxx

________________________________________

How Will You Know the Strategy Has Been Successful?

xxxxxx

________________________________________

Expected Impact:

xxxxx

# data[].attributes.body
Student Lanyard Compliance

Specific:
To improve student safety and identification on campus, all students will be reminded and encouraged to wear their college-issued lanyards at all times while on site.

Measurable:
Achieve a 90% visible lanyard compliance rate among students during random weekly spot checks conducted by staff.

Achievable:
Staff will deliver short reminders during lessons, display posters around campus, and send out a weekly email bulletin to reinforce the message. Student ambassadors will also support by modelling good practice.

Relevant:
Wearing lanyards is essential for safeguarding, ensuring only authorised individuals are present on campus, and helping staff and students identify each other quickly.

Time-bound:
This initiative will run for 6 weeks, starting Monday 21st October, with weekly monitoring and a final review on Friday 29th November.

# data[].attributes.body
Strategy: Are AI generated TLA strategies based on learner experience and self assessment valid to be used in lessons.

How to know if the strategy has been successful?

Mainly to check learning has taken place and the strategies has helped develop knowledge and skills for that individual learner.

- a. Alignment with learning objectives:

The strategy must match the intended outcomes of the lesson. If AI suggests activities that don’t reinforce the curriculum goals, its usefulness is limited.

b. Evidence-based principles:

Valid strategies rely on well-established pedagogy (e.g., scaffolding, spaced repetition, collaborative learning). AI can propose creative combinations, but you need to ensure they are pedagogically sound.

c. Adaptation and professional judgment:

AI outputs are suggestions. Teachers should review and adapt them to students’ context, prior knowledge, and classroom dynamics.

2. Measuring Success

You can assess whether the AI-generated TLA strategy has been effective through multiple lenses:

a. Learner engagement:

Are students actively participating in activities? Are they motivated?

Tools: observation, participation metrics, self-reported engagement.

b. Learning outcomes:

Compare pre- and post-assessments, quizzes, or assignments to see if learning objectives were achieved.

c. Self-assessment consistency:

Check if learners’ self-reported improvements match measurable gains. AI strategies based on self-assessment should ideally close gaps highlighted in those assessments.

d. Reflection and feedback:

Gather qualitative feedback from students: Did the activity help them understand better? Did it cater to their experience level?

3. Expected Impact

If implemented effectively, AI-personalized TLA strategies can:

1. Increase learner engagement: Activities matched to students’ experience levels are less likely to be frustrating or boring.

2. Support differentiated learning: AI can generate multiple paths for students with varying skills and learning styles.

3. Improve learning efficiency: Targeted activities address learners’ weak areas faster.

4. Encourage metacognition: Self-assessment involvement can enhance reflection and ownership of learning.

# data[].attributes.headline
Differentiating contextual art research sources for levels 2 & 3 leaners

# data[].attributes.subject
Differentiating contextual art research sources for levels 2 & 3 leaners

# data[].attributes.body
Melissa Hill and Jenny Tidmarsh

Use microphone to record discussion and produce transcript with feedback and action plan summarised.

Trial and error, learners will recognise gaps in skills and knowledge. They will receive feedback in a more timely manner. Reduces time for assessors writing notes.

# data[].attributes.headline
Can you take a document with marks and compare it against a learner's work piece and supply marks for their work.

# data[].attributes.subject
Can you take a document with marks and compare it against a learner's work piece and supply marks for their work.

# data[].attributes.body
Yes, I can do that! If you upload:

1. A marked reference document – showing how marks were awarded.

2. A learner’s work piece – the submission you want assessed.

I’ll then:

Compare the learner’s work against the reference.

Identify similarities, differences, and areas for improvement.

Suggest a mark based on the reference criteria.

You can upload files in formats like Word, PDF, or images. Once I have both, I’ll begin the comparison and provide a detailed breakdown along with a suggested mark.

# data[].attributes.body
Use AI to reduce marking workload whilst ensuring that learners receive personalised feedback. Students self assess against their personalised targets. The outcome of this would be individualised feedback against targets and next steps and challenge activities.

# data[].attributes.body
To use AI to develop my teaching resources in particular for learners who have dyslexia and ESOL