Smart Learning Experience Room (SLER)

An exclusive, integrated teaching-and-learning space that combines Instructional Design, AI-enabled support, and Learning Analytics—turning any class into a measurable learning experience.

Presenter: Hisyam Hashim Theme: From Smart Classroom → Intelligent Learning Experience Date: __________

“Smart devices do not guarantee smart learning.” SLER aligns space + pedagogy + technology + data to improve learning outcomes, engagement, and teaching efficiency.

4

Experience Modes

1

Flagship Room (Pilot)

3

Core Layers

100%

Evidence-Driven

Speaker Notes

Open with a strong statement: “Technology ≠ learning.” Briefly define SLER as a flagship space for measurable learning experiences.

Why “Smart Classrooms” often fail to produce impact

1) Tool-Centric

Devices are installed without a pedagogical blueprint.

Smart TV becomes a projector replacement
No learning design alignment

2) No Data

Engagement and mastery are not visible or measurable.

No real-time learning evidence
Assessment data is delayed

3) Fragmented Workflow

Teaching, activities, and assessment are disconnected.

Multiple platforms, no integration
High admin load for lecturers

Vision: SLER as a flagship “exclusive” space

Not a typical classroom. SLER is a studio-grade learning environment that supports high-impact teaching, collaboration, and evidence-based assessment.

What makes it exclusive?

Designed for premium learning experiences
Integrated AI + analytics workflow
Prototype room for institutional scaling

Who benefits first?

Strategic courses / flagship programs
Micro-credential & CBET delivery
Industry / international co-teaching

Core Promise “Every session generates learning evidence—engagement, misconceptions, mastery—so teaching decisions become data-informed.”

↑

Engagement & Participation

↓

Administrative Load

✔

Competency Evidence

◎

Hybrid / Global Ready

SLER Architecture: Space + Pedagogy + Technology

Layer 1 — Smart Space

Physical environment that supports active learning.

Flexible seating (pods / U-shape)
Collaboration zones
Capture-ready lighting & audio

Layer 2 — Pedagogical Intelligence

Instructional Design blueprint drives every session.

Outcome-based learning
Structured activities & feedback
Assessment aligned to competencies

Layer 3 — Smart Tech & Analytics

AI-enabled support + learning evidence.

AI teaching assistant (support)
Real-time polling & quizzes
Learning analytics dashboard

Key Differentiator

Technology is selected to serve pedagogy—not the other way around.

Pedagogical Blueprint (ID-led)

SLER adopts a repeatable learning flow where content, activity, feedback, and assessment are aligned.

Session Flow

Attention & relevance
Clear outcomes & success criteria
Guided practice + collaboration
Immediate feedback
Evidence capture (analytics)

Design Principles

Outcome-based & competency-focused
Active learning by default
Feedback loops are built-in
Accessibility & inclusivity

“The room is not smart. The learning design is.” SLER standardizes high-impact teaching patterns so any lecturer can run premium sessions confidently.

Aligned

Outcomes → Activities → Assessment

Fast

Feedback & iteration

Fair

Rubric-driven evaluation

Visible

Learning evidence captured

AI in SLER: Assist, not replace

Before Class

Generate lesson outline aligned to outcomes
Create question banks (HOTS)
Prepare adaptive activities

During Class

Real-time polls & formative checks
Instant clarifications & examples
Participation prompts

After Class

Summaries & key takeaways
Feedback suggestions (rubric-based)
Learning gaps identification

Governance & Ethics (recommended)

Privacy-by-design, transparent AI usage, academic integrity safeguards, and approved tools list.

Learning Analytics Dashboard

SLER turns learning into visible indicators for improvement, quality assurance, and accreditation evidence.

Real-time Signals

Engagement checks (polls, responses)
Misconception hotspots
Progress by outcome

Post-session Insights

Mastery distribution
At-risk learners (support planning)
Teaching improvement recommendations

Evidence Pack (for QA / Audit) Attendance + activity logs + assessment outcomes + feedback loop documentation → stored as proof of learning impact.

Live

Formative checks

Traceable

Outcome evidence

Actionable

Intervention triggers

Reportable

QA-ready summaries

SLER Usage: 4 Experience Modes

Mode 1 — Smart Teaching

High-impact lectures + continuous checks for understanding.

Interactive micro-lessons
Instant misconceptions correction

Mode 2 — Collaborative Learning

Problem-based learning, design thinking, case studios.

Group pods + facilitation
Evidence captured by task outcomes

Mode 3 — Hybrid & Global Classroom

Co-teaching, international sessions, industry engagement.

Studio-grade capture
Remote collaboration

Mode 4 — Assessment & Micro-credential

Competency-based tasks with rubric & analytics.

Performance evidence
Verifiable outcomes

Strategic Value to the Institution

University

Signature innovation showcase
Quality assurance evidence
Stronger branding & partnerships

Lecturers

Reduced repetitive work
Structured high-impact templates
Better feedback efficiency

Students

Personalized learning support
Clear competency targets
Higher engagement & retention

Outcome Focus

SLER is measured by learning results, not by the number of devices installed.

Implementation Roadmap (Practical & Scalable)

Phase 1 — Pilot (1 flagship room)

Select 2–3 courses for trial
Deploy basic capture + interaction tools
Create session templates & rubrics
Run analytics baseline

Phase 2 — Optimization

Refine workflows and dashboard
Train lecturer champions
Produce evidence report for management

Phase 3 — Scale

Replicate blueprint to more rooms
Standardize “SLER-ready” teaching kits
Institution-wide analytics standards

Success Metrics

Engagement improvement
Mastery by outcomes
Lecturer workload reduction
QA-ready evidence pack

Risk Management & Governance

Ensure sustainability: policy, training, maintenance, and ethical AI deployment.

Risks

Tool overload & low adoption
Privacy and data governance concerns
Maintenance & ownership unclear
AI misuse / academic integrity issues

Controls

Approved tool stack + training
Privacy-by-design + consent model
Clear SOP: who owns what
Integrity guidelines + assessment design

Governance Model (Suggested) SLER Committee: Academic Lead (ID), Technology Lead, QA Lead, Data/Privacy Lead, Operations Lead.

SOP

Standard operating procedures

Policy

AI & data governance

Training

Lecturer enablement

Support

Ops & maintenance

Call to Action

Let’s build one flagship SLER to prove impact fast—then scale using a repeatable blueprint.

What we need today

Approval for 1 pilot room
Selection of pilot courses
Basic tool stack & support ownership
Success metrics agreement

What you get (in 6–10 weeks)

Operational SLER room
Teaching templates & rubrics
Analytics dashboard baseline
Evidence report for management

Closing Line “We are not building a smart room. We are building a measurable learning experience.”