Welcome to AI in Digital Learning, a brand-new 10-part article series from Pixel Spark. Across this series, we’ll explore how artificial intelligence is reshaping the future of digital education — from upgrading SCORM into adaptive smart learning, to transforming instructional design with AI-driven tools, and making learning more accessible, inclusive, and engaging.

New articles will be released every Monday and Thursday, so check back regularly to follow the series and discover practical insights, real-world examples, and strategies you can apply in your organisation.

Traditional e-learning often takes a “one-size-fits-all” approach. AI changes this by creating adaptive pathways tailored to individual learners. Platforms like DreamBox Learning and Knewton analyse learner behaviour in real time, adjusting the pace and content based on performance.

Research shows that adaptive learning powered by AI significantly improves engagement and outcomes (Training Industry, 2023). In corporate training, AI can recommend modules based on role, prior training, or compliance requirements — ensuring time is spent where it’s most valuable.

For instructional designers, AI is becoming an essential co-designer.

Generative AI tools can:

• Draft assessment questions and case studies.
• Summarise large volumes of technical information.
• Suggest multimedia elements and translations.

A study published in TechTrends demonstrated how generative AI was used to produce course maps and assessment items for a graduate-level makerspace course. While human oversight remained vital for quality assurance, AI saved considerable development time (Springer, 2024).

AI is also reshaping learner support. Intelligent tutoring systems and chatbots provide real-time help, guiding learners through complex tasks or answering common questions. Some universities have deployed AI tutors to handle up to 40% of student queries, freeing academic staff for deeper engagement.

In corporate environments, AI assistants can be embedded in e-learning modules to provide hints, track errors, or suggest further reading — ensuring that learning feels less isolating and more interactive.

One of the persistent challenges in digital learning is completion rates. AI-powered personalisation and real-time feedback are proving to increase both engagement and retention.

A study in Computers & Education found that AI-driven personalised learning improved learner motivation and self-efficacy among adult learners (ScienceDirect, 2025).

While the opportunities are immense, there are risks. AI must be transparent, bias-free, and compliant with privacy regulations such as GDPR.

In accredited training environments, content generated or adapted by AI must still meet established quality standards such as Quality Matters or ISO 21001. Human oversight ensures that AI outputs align with learning objectives and accreditation requirements.

• Training Industry (2023). Personalised learning at scale: How AI is shaping L&D.
  https://trainingindustry.com/articles/personalization-and-learning-pathways/personalized-learning-at-scale-how-ai-is-shaping-ld/
• Springer (2024). Utilising generative AI for instructional design. TechTrends.
  https://link.springer.com/article/10.1007/s11528-024-00967-w
• ScienceDirect (2025). AI-powered personalised learning: Enhancing self-efficacy, motivation, and learning engagement among adult learners. Computers & Education.
  https://www.sciencedirect.com/science/article/pii/S0023969025000360
• International Organization for Standardization (2018). ISO 21001:2018 — Educational organisations management systems. ISO. https://www.iso.org/standard/21001