AI in Education Course: Transforming Teaching and Learning – Manama, 20–24 Dec 2026

AI in Education: Transforming Teaching and Learning is a 1-week training course for school and university leaders, academic heads, learning designers and education technology managers, ending with an institutional AI adoption roadmap for classrooms, assessment and learner records. Generative tools have entered homework, marking and lesson design faster than academic policies have adapted, so institutions must decide where these tools help learners and where they weaken learning. Jupiter Prime Training runs this course.

Why AI in Education Matters Now

Three forces shape the subject today. Learners already use chat assistants for essays and revision, which unsettles coursework design and academic integrity. Teachers face heavy marking and planning loads that adaptive platforms and feedback assistants promise to lighten. Parents, governors and school inspectors expect learner records to stay private and automated judgements to stay fair. An institution that handles this well frees teacher time and personalises practice; one that handles it badly faces plagiarism disputes, biased grading and lost trust. The course draws on ISO/IEC 42001 for AI management systems and the NIST AI Risk Management Framework (NIST AI RMF) for judging classroom tools.

Who Gains Most from AI in Education

• Senior leaders who set teaching strategy and approve digital learning investments
• Academic heads who own curriculum, coursework rules and examination integrity
• Learning designers who build courses, rubrics and online learning sequences
• Education technology managers who select, integrate and support learning platforms
• Quality and safeguarding leads who protect learners and their records

A school group, faculty or college gains most when its academic, digital and safeguarding leads attend together and agree one stance on generative tools in classrooms.

AI in Education Capabilities

Participants test tutoring assistants, marking aids and adaptive courseware against real lesson plans and rubrics, and decide where a teacher stays the final judge.

• Assess adaptive learning platforms against curriculum goals and learner needs — academic heads
• Design assessment tasks that stay valid when learners use chat assistants — examination boards
• Specify AI-assisted feedback rules for essays and problem sets — teaching staff
• Build a classroom generative AI policy with permitted, guided and banned uses — senior leaders
• Apply learner privacy and fairness checks to edtech procurement — safeguarding leads
• Communicate the institutional roadmap to teachers, parents and learners — governors

Key AI in Education Terms

• Adaptive learning — software that adjusts content, pace and practice questions to each learner's responses and mastery.
• Learning analytics — the measurement and interpretation of learner activity records to improve teaching and spot learners at risk.
• Intelligent tutoring system — an assistant that guides a learner step by step through problems with hints and targeted explanation.
• Automated feedback — comments on learner work produced by software, ideally checked and adjusted by the teacher before release.
• Academic integrity — the expectation that submitted work honestly reflects the learner's own effort and properly credits any help.
• Assessment redesign — changing tasks, conditions and marking criteria so they still measure learning when generative tools exist.

AI in Education Day by Day

Day 1: The Changing Classroom and Campus

• Generative Assistants in Homework — Effects on Learning Habits
• Teacher Workload Mapping — Where Automation Frees Time
• Schools and Universities Case Comparison — Contrasting Uses
• Learner Expectations Survey — Readiness of Students and Staff
• Digital Maturity Audit — Platforms, Skills and Connectivity

Day 2: Personalised Learning and Tutoring

• Adaptive Learning Platforms — Mastery Paths and Pacing Choices
• Intelligent Tutoring Systems — Hints, Scaffolds and Misconceptions
• Learning Analytics Dashboards — Early Warnings for Struggling Learners
• Accessibility Support — Text-to-Speech, Captioning and Translation
• Lesson Planning Assistants — Teacher Review Before Classroom Use

Day 3: Assessment, Feedback and Integrity

• Assessment Redesign — Oral Defences, Drafts and In-Class Tasks
• Automated Feedback on Essays — Rubric Alignment and Teacher Checks
• Authorship Detection Limits — False Accusations and Appeals
• Academic Integrity Codes — Declaring Assistant Use in Submissions
• Marking Moderation Samples — Consistency Between Human and Machine

Day 4: Policy, Learner Privacy and Fairness

• Classroom Generative AI Policy — Permitted, Guided and Banned Uses
• Learner Record Protection — Consent, Retention and Vendor Access
• NIST AI RMF Map Function — Risks of Edtech Tools to Learners
• ISO/IEC 42001 Controls — Roles, Approval and Incident Handling
• Bias Checks in Grading Aids — Fair Results Across Learner Groups

Day 5: Practising the Institutional AI Adoption Roadmap

• Classroom Audit and Workload Map From Day 1 — Starting Position
• Platform and Tutoring Choices From Day 2 — Learning Priorities
• Assessment and Integrity Rules From Day 3 — Coursework Changes
• Policy and Privacy Safeguards From Day 4 — Learner Protection
• Institutional AI Adoption Roadmap — Phased Plan for Approval

Applying AI in Education After the Course

• Redesign one coursework task with a draft stage and an oral check, then compare marks with last year's version.
• Run a staff and learner survey on chat assistant use and present the results with a draft classroom policy to the academic board.

AI in Education Questions (FAQ)

What is AI in education and why does it matter now?

AI in education is the use of adaptive platforms, tutoring assistants and feedback tools in teaching, learning and assessment. It matters now because learners already use generative assistants, and institutions must protect learning quality, integrity and privacy.

Which roles gain most from AI in education training?

Senior leaders, academic heads, learning designers and education technology managers gain most. Quality and safeguarding leads also benefit, because they protect learner records and handle integrity cases.

How does AI in education differ from a general AI for managers course?

It applies the methods to classrooms, coursework and learner records rather than to commercial operations. You redesign assessment, choose tutoring platforms and write a classroom generative AI policy for a teaching institution.

What do you bring back from AI in education training?

You bring back an institutional AI adoption roadmap. It sets platform choices, assessment changes, integrity rules and learner privacy safeguards in phases your academic board can approve.

AI in Education: The Way Forward

The institutional AI adoption roadmap informs the first decision: which courses and year groups trial adaptive practice and revised coursework next term. Academic heads, digital leads and safeguarding staff use it to brief teachers and governors. After the first assessment cycle, revisit the integrity rules and the feedback checks, because learner habits change once the policy is known.