Programme Parameters
Graduate / Intermediate
8 Weeks (80 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Employee Training and Development shows how machine learning is modernising how organisations build their people’s skills. You learn to apply AI across corporate L&D: personalising learning paths, recommending content, analysing skills gaps, and adapting training to the individual. The course connects these to measuring learning impact and workforce capability, and to the fairness and privacy that using employee data requires. Grounded in real L&D challenges, it turns training from one-size-fits-all into targeted development. You finish able to apply AI to a workplace-learning problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in employee training and development — personalised learning paths, skills analytics, adaptive content and measuring L&D impact.
📋 Course Objectives
1. Personalise learning paths with AI.
2. Analyse skills gaps and needs.
3. Recommend and adapt training content.
4. Measure learning impact and capability.
5. Address employee-data privacy and fairness.
👥 Who Should Enroll?
• L&D and HR professionals
• Corporate-training and talent teams
• People-analytics staff
• Students of workplace learning
🚀 Key Learning Outcomes
• An understanding of AI in corporate L&D.
• A skills-analytics perspective.
• A measurable, learner-centred approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Skills Analysis and Gap Identification
• Skills taxonomies and inferring skills from role and project data
• Gap analysis against future capability requirements rather than current roles
• Distinguishing a training problem from a process or incentive problem
Instructional Design With Automation
• Learning objectives and assessment alignment before content generation
• Generative AI for content drafting, and subject-matter review workflow
• Simulation and scenario-based practice for judgement-heavy roles
Adaptive Pathways at Work
• Recommendation of learning content and its cold-start problem
• Spacing and reinforcement scheduled around real work demands
• Coaching assistants and the boundary with manager responsibility
Proving Training Worked
• Kirkpatrick levels and why most programmes stop at reaction data
• Transfer to job performance and the measurement designs that detect it
• Control groups and staged rollout as practical evaluation tools
Data, Equity and Trust
• Learner data privacy and the reuse of training data in performance decisions
• Accessibility and language inclusion across a distributed workforce
• Avoiding recommendation systems that entrench existing advantage
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
Scholar Feedback & Reviews
Based on 0 scholar submissions
Rating Breakdown
No verified reviews published yet. Be the first to share your academic experience.
Leave Scholar Feedback
Your rating will help prospective scholars. Ratings below 3 stars are routed privately to the faculty mentor for immediate response.
