SERIES: AI in Digital Learning – Article 2
From SCORM to Smart Learning:
Using AI to Enhance Course Delivery
Article by Pixel Spark Ltd. Published 6th October 2025
Introduction
For more than two decades, SCORM (Sharable Content Object Reference Model) has been the backbone of e-learning. It standardised packaging, compatibility, and tracking. But in a world where learners expect personalised, interactive, and data-rich experiences, static SCORM packages are beginning to show their limitations. AI offers a way forward — transforming SCORM into the foundation of “smart learning”.
The Limitations of Traditional SCORM
- Linear content: Most SCORM packages deliver the same material to every learner.
- Minimal adaptivity: Beyond completion and score data, SCORM offers little insight into how learners actually engage.
- Static structure: Updating content is often time-consuming and lacks flexibility.
This rigidity is increasingly misaligned with modern expectations for learner-driven, adaptive experiences.
Real-World Examples
- Personalised SCORM delivery: LMS Collaborator demonstrated how SCORM content built in Articulate Storyline could be personalised by inserting user data such as role, department, and even learner names into the course (Collaborator, 2023).
- Interactive video conversion: Blue Whale Apps re-engineered a static, text-heavy marketing course into interactive SCORM video modules, improving learner engagement and completion rates (Blue Whale Apps, 2022).
- Beyond “click next” courses: Modern SCORM projects now include branching logic and conditionals, enabling different learning paths depending on user responses (LMS Portals, 2023).
How AI Enhances SCORM Delivery
- Predictive analytics: Analyses engagement data to anticipate learner difficulties.
- Content recommendations: Suggests modules or resources based on performance.
- Automated tagging & metadata: Improves discoverability and reuse of SCORM assets.
- Adaptive assessments: Adjusts difficulty dynamically inside SCORM modules.
Accreditation and Standards
AI-driven enhancements must still comply with:
- SCORM standards (ADL Initiative).
- Emerging specifications like xAPI (Tin Can API) for granular tracking.
- Compliance frameworks such as ISO 21001:2025 or industry-specific requirements.
Privacy is also key: using learner profile data must align with GDPR and other data protection laws.
Conclusion
SCORM may have started as a technical standard, but with AI it can evolve into a dynamic, learner-centred ecosystem. By integrating predictive analytics, adaptive pathways, and personalisation, organisations can unlock the next generation of e-learning.
The future of SCORM is not static — it’s smart.
References
- Collaborator (2023). Personalised SCORM: A guide with examples.
https://collaborator.biz/en/blog/personalized-scorm-guide/ - Blue Whale Apps (2022). SCORM interactive video training for marketing management.
https://bluewhaleapps.com/case-studies/scorm-interactive-video-training-for-marketing-management - LMS Portals (2023). Beyond click-next: Creative ways to make SCORM courses interactive.
https://www.lmsportals.com/post/beyond-click-next-creative-ways-to-make-scorm-courses-interactive - ADL Initiative (n.d.) SCORM overview.
https://adlnet.gov/projects/scorm/ - ADL Initiative. (n.d.). Experience API (xAPI).
https://adlnet.gov/projects/xapi/ - International Organization for Standardization (2025). ISO 21001:2025 — Educational organisations management systems.
https://www.iso.org/standard/21001.html
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