AI Content Workflows - LearningMate

✓ Stage 1

Course Brief

✓ Stage 2

Blueprint

▶ Stage 3

Content Draft

Stage 4

Assessment

Stage 5

Media & QA

Stage 6

LMS Build

6

Modules · 18 units

33%

Workflow complete

5

AI outputs generated

📁 Course Brief — Inputs Provided

Complete

Faculty inputs received

✓

Learning outcomes (6 programme-level)

Provided by Dr Amara Osei-Bonsu, University of Kestrel

✓

Faculty lecture notes (18pp unstructured)

Focus: algorithmic bias, GDPR, healthcare AI ethics

✓

Annotated reading list (12 sources)

Mix of academic and industry case studies

✓

Partner style guide (University of Kestrel)

Plain language standard · 250-word content blocks

!

Assessment brief

Partial — formative only confirmed; summative TBC

👥 Project Team & Review Gates

Name	Role	Review Gate	Status
Dr A. Osei-Bonsu	Faculty / SME	Blueprint, Content	Active
Priya Menon	Learning Designer	All stages	Active
Tom Ashworth	Editor / QA	Draft, QA	Pending
Jess Wren	Accessibility	QA, LMS	Pending
LM AI	AI Workflow Support	All stages	✦ Active

Next action: Priya Menon to review Unit 1–3 content drafts. SME sign-off required before Stage 4 (Assessment).

🗺️ AI-Generated Module Structure — Approved by Faculty

✓ Dr Osei-Bonsu approved 14 May

Generated from faculty notes, learning outcomes, and partner brief. Learning designer refined activity sequencing before SME approval.

#	Module Title	Units	Key Outcomes	Activity Type	Status
M1	What is Data Ethics?	3	LO1, LO2	Read → Discuss → Reflect	✦ Draft in progress
M2	Algorithmic Bias & Fairness	3	LO2, LO3	Case study → Apply → Assess	Queued
M3	Data Privacy & GDPR	3	LO3, LO4	Scenario → Decision → Debate	Queued
M4	AI Ethics in Healthcare	3	LO4, LO5	Case study → Simulation → Review	Queued
M5	Governance & Accountability	3	LO5, LO6	Framework → Apply → Evaluate	Queued
M6	Ethics in Practice	3	LO1–LO6	Integrated case → Portfolio	Queued

🎯 Learning Outcomes Map

AI Generated · Approved

Outcome	Level	Mapped to
LO1	Bloom's L2	M1, M6
LO2	Bloom's L3	M1, M2
LO3	Bloom's L4	M2, M3
LO4	Bloom's L4	M3, M4
LO5	Bloom's L5	M4, M5
LO6	Bloom's L6	M5, M6

📊 Learner Time Estimates

✦ AI Estimated

Component	Per Unit	Per Module
Reading / Content	20 min	60 min
Activities	15 min	45 min
Reflection / Discussion	10 min	30 min
Formative check	5 min	15 min
Total per module	—	~150 min

📥 Faculty Input — Module 1, Unit 1

Received

Raw faculty notes (excerpt)

Data ethics = study of ethical issues arising from data collection, storage, analysis and use. Covers: privacy, consent, bias, accountability, transparency.

Key tension: between utility of data and protection of individuals. Not just legal compliance — moral responsibility.

Case I like: Amazon hiring algorithm (2018) — trained on CVs from past 10 years, penalised women. Classic example of bias in → bias out.

Also EU AI Act context — tiered risk. Students need to understand why governance matters.

Tone: this is PG level, they should be able to engage critically, not just describe.

Generate content for

🔧 Generation Settings

Partner style	University of Kestrel
Max block length	250 words
Reading level	Postgraduate (active voice)
Bloom's target	L2–L3 for M1
Human review	Required before Stage 4

✍️ AI Content Draft — M1 Unit 1

✦ Ready to generate

✦

AI draft will appear here

Click "Generate Content Draft" to create a learner-ready
draft from the faculty notes using your partner settings.

AI Draft — M1 Unit 1 · Introduction to Data Ethics

Learning designer review

LD note: Opening paragraph adjusted — "moral calculus" replaced with plainer language per Kestrel style guide. Faculty case study preserved as-is. Discussion prompt added.

Data ethics is the study of ethical questions that arise when data is collected, stored, analysed, and used to make decisions. It goes beyond legal compliance — it asks us to consider the human consequences of working with data, and our responsibility to the people that data represents.

Three tensions sit at the heart of data ethics: the value of data to organisations and society, the rights of individuals whose data is collected, and the question of who is accountable when things go wrong. Understanding these tensions is the foundation for everything that follows in this module.

A real example: In 2018, Amazon scrapped an AI hiring tool that had been trained on CVs from the previous ten years. Because those CVs came predominantly from male applicants, the algorithm learned to penalise women — downranking CVs that included the word "women's" and graduates of all-women colleges. The bias in the training data became bias in the outcome.

This case illustrates a core principle: AI systems do not create bias — they reflect and amplify the bias already present in the data and decisions that shaped them.

Reflection prompt: Think of a context where you regularly encounter data-driven decisions. What ethical questions might those decisions raise? We will return to your example throughout this module.

Learning designer review: Priya Menon — content accuracy and learner experience check

✓ Approved — moves to SME review

📝 Assessment Brief

Partial

Assessment type	Formative quiz (confirmed) Peer discussion rubric (confirmed)
Outcomes targeted	LO1, LO2, LO3 for M1
Bloom's level	L2–L4 (remember, understand, apply)
Items required	8 MCQ + 1 discussion rubric
Partner standard	University of Kestrel — formative only for M1

📋 AI-Generated Assessment — M1

✦ Ready to generate

📝

Assessment items will appear here

AI will generate MCQ items, rubric, and outcome alignment mapped to your brief.

AI Assessment — 4 of 8 MCQ · M1 · Data Ethics Foundations

Q1 · LO1 · Bloom's L2 — Understand

Which of the following best describes the concept of "data ethics"?

A. The legal rules governing data collection under GDPR

B. ✓ The study of moral questions arising from how data is collected, analysed, and used

C. A set of technical standards for data storage and security

D. The process of anonymising personal data before analysis

✦ AI Generated · Mapped LO1

Q2 · LO2 · Bloom's L3 — Apply

Amazon's 2018 hiring algorithm downranked CVs from women. This is an example of which core data ethics problem?

A. A privacy breach under GDPR Article 5

B. A transparency failure in the recruitment process

C. ✓ Algorithmic bias arising from biased training data

D. Inadequate data minimisation during model training

✦ AI Generated · Mapped LO2

Q3 · LO1 · Bloom's L2 — Understand

What is the key tension at the heart of data ethics, as described in Unit 1?

A. Between open-source and proprietary data systems

B. ✓ Between the value of data to organisations and the rights of the individuals it represents

C. Between data scientists and legal compliance teams

D. Between cloud storage efficiency and on-premise security

✦ AI Generated · Mapped LO1

Q4 · LO3 · Bloom's L4 — Analyse

A healthcare provider uses an AI model to predict patient readmission risk. The model was trained on historical data that underrepresents patients from lower-income postcodes. What is the most likely ethical consequence?

A. The model will breach GDPR data minimisation requirements

B. The model will be too slow to process real-time patient data

C. ✓ The model will systematically underpredict readmission risk for lower-income patients, widening health inequalities

D. The model will require manual override for all high-risk predictions

✦ AI Generated · Mapped LO3 · Bloom's L4

Faculty review: Dr Osei-Bonsu — accuracy check and cognitive level validation required

✅ Automated QA Checks — M1 Content

✓

Readability — Kestrel Plain Language Standard

All content blocks within Grade 13 reading level. 3 passive voice instances flagged for LD review.

PASS

✓

Word count — 250-word block limit

Unit 1: 242 words ✓ · Unit 2: 238 words ✓ · Unit 3: pending draft

PASS

!

Outcome alignment — LO mapping check

Reflection prompt in Unit 1 not explicitly mapped to LO1 or LO2. Recommend adding outcome tag before QA sign-off.

FLAG — Action required

!

DEI review — demographic representation

Amazon case study only. Recommend adding a non-US example for balance. M4 (Healthcare AI) should feature Global South context.

FLAG — Recommendation

✓

Tone & register — postgraduate level

Appropriate register throughout. Active voice used in 87% of sentences. No jargon flagged without definition.

PASS

♿ Accessibility Checks — WCAG 2.1 AA

2 items pending

✓

Text contrast ratios

All learner-facing text passes AA contrast ratio (4.5:1+)

PASS

!

Image alt text — M1 infographic

AI draft alt text: "Diagram showing data ethics tensions." LD review needed — too generic for screen reader users.

FLAG — Needs rework

!

Video captions — discussion intro clip

Captions not yet provided. Auto-caption draft generated — requires human accuracy review before upload.

FLAG — Pending

✓

Metadata & LMS tagging

SCORM metadata generated. Course tags, keyword taxonomy, and unit labels confirmed against Kestrel LMS schema.

PASS

📤 QA Sign-Off Progress

Reviewer	Area	Status
Priya Menon (LD)	Content · Alignment	In progress
Tom Ashworth (Ed)	Style · Readability	Awaiting draft
Jess Wren (Access.)	WCAG · Alt text · Captions	Awaiting media
Dr Osei-Bonsu (SME)	Content accuracy	Awaiting LD approval

QA completion: 20% · Content draft approval required to unblock remaining reviewers

💡 LM AI — QA Summary & Recommended Actions

✦ AI Summary

AI QA Summary · M1 · Introduction to Data Ethics

Content quality for Module 1 is on track. Two flags require human action before the module can advance to LMS build. The outcome alignment flag (reflection prompt) is a quick fix — LD to add an LO tag. The DEI flag is a recommendation for Modules 2–4 rather than a blocker for M1. Accessibility checks (alt text, captions) should be scheduled with Jess Wren once media assets are confirmed. No content accuracy issues detected that require returning to faculty at this stage, but SME sign-off on the Amazon case study framing is advised before final QA approval.