The Future of AI: Six Impacts of AI in 2026

As we close out 2025, one thing is unmistakable: AI has moved from promise to practice across education and workforce learning. What once felt speculative is now operational, embedded in how learners engage, how educators teach, and how institutions measure impact. At Cengage Group this shift translated into real innovation across our portfolio. From empowering faculty with real-time insights to helping students personalize their learning journeys, we focused on turning AI into something accessible, adaptable, and outcome-driven.

Beyond product innovation, we forged strategic partnerships to accelerate progress—joining forces with LinkedIn Learning to broaden access to AI and cybersecurity training and collaborating with AWS to define the future of AI in education. These milestones underscore our commitment to not just keeping pace with change but leading it.

As we look ahead to 2026, the question becomes: what’s next? Based on what we’ve learned this year and where the industry is headed, here are my six predictions for how AI will continue to shape education in the year to come.

Six Impacts of AI in 2026

1. The Rise of LLM-Agnostic AI in EdTech

The competition between the Big AI models will continue to heat up, with tech leaders launching new models and surpassing each other at an incredibly fast pace.  This is exciting, but it also means no single model will stay on top for long. For edtech providers, the real advantage comes from being LLM-agnostic: building flexible systems that can work with any model. Staying adaptable isn’t just a tech decision; it’s a strategy to make sure learners and educators always get the best experience as technology evolves. In practice, this shifts competitive advantage away from model selection and toward architecture, orchestration, and educational alignment.

2. Learning Moves from Passive to Interactive

AI is rapidly changing the way students consume content and engage with learning materials from passive consumption to active engagement. Learning will shift from something students read or watch to something they participate in. We are already seeing this with Student Assistant, where learners navigate content conversationally, ask questions in context and receive explanations aligned to pedagogy. Looking ahead, these experiences are expected to become deeply personalized—content adapting to mastery level and learning preferences in real time. Search will feel more like dialogue. Assignments will resemble coaching. Feedback will move from generic to bespoke. Students will come to expect systems that understand their intent, anticipate where they are stuck and deliver the right support at the right moment.

3. AI Amplifies Educator Impact

For educators, AI’s greatest value lies in augmentation, not replacement. While automation can reduce administrative burden, the real opportunity is to elevate educator impact. Our Instructor Assistant work makes this clear. AI can help design lesson plans, generate formative assessments, differentiate instruction and surface insights about where students need support. This creates more time for what teachers do best: building relationships, coaching, and guiding deeper learning.  The classroom of the future is defined by AI working alongside educators, not instead of them.

4. Education Shifts to Outcome-Centric Models

In 2026, I believe that education will shift from content-centricity to outcome-centricity at scale. AI is expected to play a role in enabling institutions to measure and improve learning outcomes continuously rather than episodically. From curriculum design to student support, adaptive systems and real-time data help optimize for results rather than inputs.  This will require institutions to rethink success metrics, instructional design, and student support models in more integrated ways.

5. Accelerating Equity and Closing Learning Gaps

AI holds significant promise for closing learning gaps when applied responsibly. Early evidence from our beta pilots shows measurable improvements in comprehension, confidence and persistence. The potential to democratize high-quality learning support is enormous. What keeps me up is not the technology; it is the pace at which institutions and organizations must adapt. Governance, data readiness, responsible AI frameworks and change management are now foundational capabilities. The organizations that succeed will be those that can align people, processes, data and technology at the same time.

6. The Rise of Small Language Models

The biggest overlooked shift is the rise of vertically tuned small language models built on proprietary content and pedagogy. While the market focuses on general-purpose models, the real differentiation in education will come from domain-specific models that encode instructional design, disciplinary vocabulary, reasoning patterns and assessment rubrics. These models have the potential to outperform general systems in accuracy, trust and educational alignment, and they will become core IP for every learning company.  In this next phase, educational advantage will be defined less by access to models and more by ownership of domain-specific intelligence.

 

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As we enter 2026, AI is no longer an emerging trend. It is becoming the operating layer for education and workforce development. The impacts outlined above reflect both how far we have come and the strategic decisions institutions and learning companies must make next.

The organizations that lead will be those that move beyond experimentation and commit to building AI that is trusted, aligned to pedagogy, and measured by outcomes.

Want to see how these ideas evolved? Take a look back at the predictions from Darren and other Cengage Group leaders in our 2025 AI Predictions blog and explore how those early insights set the stage for today’s innovations.