2026: The Year Learning Gets Real (Again)

This blog is based on insights shared during the Digital Learning Institute webinar “What to Expect in Digital Learning in 2026,” featuring John Kilroy (Founder & Chief Growth Officer, DLI) and Dr. Luke Hobson (MIT, University of Miami, author & podcaster). 

If 2024–25 were the years of “try everything,” then 2026 is where learning teams get strategic, human, and measurable. The hype hasn’t disappeared, AI is now infused into nearly every tool we touch but the conversation has shifted from novelty to outcomes: credible content, authentic assessment, accessible experiences, and change that actually sticks. 

Below are the big signals from our webinar. What’s changing, why it matters, and how to act before January rolls around. 

How will AI transform learning design in 2026? 

As Luke put it, we’ve hit the “Clippy-on-steroids” phase: every app opens by asking how its AI can help. That omnipresence is both powerful and distracting. The lesson for L&D is intentionality. 

What’s changed: 

• Mainstream tools have quietly added serious learning features. Example: NotebookLM’s newer updates (mind maps, quizzes, comprehension checks) move it from “cool demo” to practice and feedback engine. 

• Scenario creation got real(er). Video tools (e.g., Runway; Sora-like generators) and voice/translation tools (e.g., HeyGen) can produce lifelike prompts, contexts, and multilingual assets in hours instead of weeks. 

• Browsers/agents that “do work for you” (think research copilots that can even attempt coursework submissions) are forcing a rethink of assessment integrity and design. 

What to do in 2026: 

• Treat GenAI as a power tool, not an autopilot. Put it in trained hands, within clear safety and design guardrails. 

• Bring AI into your evidence loops (draft → critique → iterate) rather than into your final deliverables. Learners should use it to generate, analyze, and test ideas then show their thinking. 

• Create an AI adoption playbook: roles, policies, approval paths, and minimal viable stacks. If everyone experiments differently, you’ll lose momentum and trust. 

Why is change management the key to AI success? 

John’s observation from a year of enterprise work is crystal clear: the organizations succeeding with AI aren’t “doing tools,” they’re running change. 

Winning patterns we see: 

• Role analysis first: who does what, where AI can safely augment, and where human judgment is non-negotiable. 

• Skills frameworks next: AI literacy (responsible use, data basics), prompt craft, evaluation skills, and accessibility. 

• Ecosystem thinking: LMS/VLE won’t vanish, but learning is happening in Teams, Slack, and social spaces. Design for the flow of work, not just the course shell. 

How to start now: 

• Launch an AI literacy uplift (DLI’s framework is a good anchor): responsible AI, ethics, data use, policy-in-course, and critical evaluation of AI outputs. 

• Pilot one process end-to-end (e.g., scenario design or feedback workflows), capture data, iterate, scale. Don’t boil the ocean. 

How should learning teams redesign assessment for an AI world? 

The end of “seven modules of MCQs + an essay” is not a prediction it’s a survival tactic. With agentic browsers and generative tools on tap, task-based, social, and reflective assessments become essential. 

What works in 2026: 

• Teach-back assessments: learners choose a concept early and must teach it (live or via video), including peer Q&A and critique of AI-suggested content. 

• Case debates & role-plays: team-based positions, rotating roles, and reflective submissions citing what peers argued and how decisions evolved. 

• Portfolio evidence: artifacts from the job (or simulated practice) with traceable iterations including AI’s contributions plus rationale and criteria. 

• Interview & critique: engage an industry expert (or SME panel), submit the interview plan, conduct it, and reflect on insights vs. AI outputs. 

Design prompts to steal: 

• “Show the steps you took (with screenshots or notes), what AI suggested, what you kept/changed, and why. Reference at least one peer’s feedback.” 

• “Record a 3-minute debrief explaining how you validated facts (or translations), including what an expert corrected.” 

Are subject matter experts being replaced by AI? 

AI can draft decent content. What it cannot fabricate is credibility, context, and lived judgment. That’s the SME’s new value proposition. 

Shift the SME role to: 

• Curation and critique over creation from scratch. 

• Just-in-time interventions inside simulations/role-plays where nuance matters. 

• Context stamps: “What would actually happen here at our company in our market, with our constraints?” 

Practical model: 

• Pre-work (AI-assisted): learners consume core materials, generate questions. 

• Simulation/role-play: conversational AI or video-driven scenario for practice. 

• SME checkpoint: 15–30 minutes of targeted feedback on decisions, with context and alternatives. 

• Evidence pack: show the evolution from first draft to SME-informed decisions. 

What’s next for micro-credentials and stackable learning? 

Micro-credentials haven’t been “wrong,” but many have been underspecified (weak definitions, thin experiences, vague outcomes). The good news: we’re seeing a reset. 

What improves in 2026: 

• Authentic, applied assessment as the norm (not “quizzes at scale”). 

• Industry co-design early, with job-linked competencies and clear transfer pathways. 

• Visibility of stackability: how 5–10–20 credit chunks aggregate towards recognized awards. 

Checklist for your next micro-credential: 

• Evidence aligns to real job outputs (deliverables your stakeholders value). 

• Assessment rubric includes AI transparency (what was used, how evaluated). 

• Learners can articulate context (why this solution fits here, not just in theory). 

• There’s a clear stacking map learners can see on day one. 

Why will accessibility become the top digital learning skill in 2026? 

The most requested skill heading into 2026 at ALT (Association for Learning Technology) events? Digital accessibility. AI won’t magically fix accessibility, but it lowers friction to do it properly. 

In practice: 

• Use AI-powered accessibility checks to flag issues early (contrast, captions, alt text, document structure). 

• Build multimodal options by default (transcripts, concise one-pagers, narrated explainers). 

• Validate automated translations with human review, especially where cultural or domain precision matters. 

How do we protect social learning in an AI-saturated world? 

If AI can “talk to me,” why bother with classmates? Because community changes behavior. Social learning has struggled inside traditional LMS forums; it thrives in the tools people already use. 

Design for social learning where it lives: 

• Shift discussions and quick share-outs to Teams/Slack channels with light structure (e.g., weekly “work-in-progress” threads, peer boosts, short video check-ins). 

• Tie every social moment to an evidence artifact (a screenshot, a snippet, a 60-second “what I tried” video). 

• Create on-ramps for quieter contributors: reaction prompts, polls, micro-reflections, and “pair-and-share” before group debates. 

What about sustainability and the cost of AI? 

There’s growing unease about the cost and environmental footprint of large-scale AI. Expect some consolidation in 2026 and be ready to answer: What do we keep if budgets tighten? 

Stay resilient by: 

• Prioritizing process improvements over fancy outputs (faster feedback cycles, better iterations, clearer rubrics). 

• Choosing interoperable tools and keeping source-of-truth content decoupled from any single vendor. 

• Teaching AI-critical thinking so learners can thrive even if the tooling changes. 

How can learning professionals prepare for 2026? 

1) Codify AI Literacy 

• Responsible use, data basics, bias & evaluation, prompt patterns, and disclosure. 

• Include a lightweight AI-in-assessment policy in every course. 

2) Redesign Two Assessments 

• Replace one essay and one quiz with a teach-back and a scenario + reflection that evidence process, peer input, and context. 

3) Reframe the SME Contract 

• Move SMEs into curation + checkpoints. Budget for brief, high-impact interventions, not long lecture builds. 

4) Make Accessibility Default 

• Bake in checks and multimodal formats from day one. Automate where sensible, human-validate where it matters. 

5) Map Stackability 

• Show learners how today’s micro-credential stacks into tomorrow’s pathway. Publish the map, not just the module. 

6) Design for the Flow of Work 

• Shift quick interactions into Teams/Slack. Keep the LMS for records, milestones, and structured assessment. 

7) Measure What Matters 

• Track career outcomes, applied artifacts, and feedback cycle speed—not just completion rates. 

What’s the big takeaway for 2026? 

We’re past the “wow” phase. This next year belongs to teams who can blend power tools with pedagogy, protect authenticity, and make learning visibly useful at work. 

Use AI to accelerate iteration, not to replace thinking. Put SMEs where their judgment matters most. Ask learners to show their process, not just submit an answer. And design communities where people want to keep showing up. 

If you’re doing those things, you’re not just ready for 2026 you’re already building it. 

Want to watch the webinar? It's now available to watch back here.