The AI tool landscape has exploded. Hundreds of tools claim to revolutionise L&D work. Most are either overhyped, poorly designed, or duplicative of better options.

After extensive evaluation, here’s my guide to AI tools that actually help L&D professionals work more effectively.

Content Creation Tools

AI has transformed content creation from slow craft to rapid iteration.

Large Language Models (ChatGPT, Claude, etc.)

What they’re good for:

• Drafting course outlines and learning objectives
• Creating assessment questions and answer options
• Writing scenario setups and branching narratives
• Generating first drafts of job aids and reference materials
• Summarising research and content for course development

What they’re not good for:

• Creating accurate technical content without expert review
• Understanding your specific organisational context
• Replacing instructional design expertise
• Producing final content without human editing

Usage tips:

• Always verify accuracy, especially for compliance or technical content
• Use as first-draft generator, not final-content producer
• Provide context about your audience and objectives in prompts
• Iterate through multiple prompt refinements for best results

AI Video Creation (Synthesia, HeyGen, etc.)

What they’re good for:

• Creating talking-head videos quickly without filming
• Producing content in multiple languages efficiently
• Updating videos without re-recording
• Generating draft videos for rapid prototyping

What they’re not good for:

• Replacing authentic human connection in important content
• Creating content that requires emotional nuance
• Any situation where AI-generated faces might undermine trust

Usage considerations:

• Be transparent when using AI-generated presenters
• Consider organisational culture fit—some audiences accept this, others don’t
• Use for appropriate content types—not everything should be AI avatar

AI Image Generation (Midjourney, DALL-E, etc.)

What they’re good for:

• Creating custom illustrations for courses
• Generating diverse representation without stock photo limitations
• Rapid prototyping of visual concepts
• Creating consistent visual styles across materials

What they’re not good for:

• Accurate technical diagrams
• Images requiring specific real-world accuracy
• Situations requiring consistent character depiction across many images

Usage considerations:

• Check licensing terms for commercial use
• Review for accuracy and appropriateness
• Be aware of potential biases in generated imagery

Learning Delivery Tools

AI is changing how learning experiences are delivered.

AI-Powered Coaching Tools

What they offer:

• Practice conversations with AI role-play partners
• Feedback on communication and presentation
• Personalised coaching recommendations
• Scalable coaching-like experiences

Evaluation criteria:

• Quality of feedback provided
• Realism of conversation interactions
• Integration with existing learning programs
• Privacy and data handling practices

Intelligent Tutoring Systems

What they offer:

• Adaptive learning paths based on demonstrated understanding
• Personalised explanations when learners struggle
• Practice with immediate feedback
• Mastery-based progression

Evaluation criteria:

• Effectiveness of adaptation algorithms
• Quality of explanations and feedback
• Subject matter coverage
• Integration with learning management systems

AI-Enhanced Assessment

What they offer:

• Automated evaluation of complex responses
• Plagiarism and AI-content detection
• Adaptive testing that adjusts difficulty
• Analysis of assessment patterns and gaps

Evaluation criteria:

• Accuracy of automated evaluation
• Validity of assessment approaches
• Fairness and bias considerations
• Transparency of scoring logic

Analytics and Measurement Tools

AI enables deeper understanding of learning effectiveness.

Learning Analytics Platforms

What they offer:

• Pattern identification across learning data
• Predictive insights about learner success
• Automated reporting and dashboards
• Integration of data from multiple sources

Evaluation criteria:

• Integration capabilities with existing systems
• Actionability of insights provided
• Privacy and security practices
• Customisation for organisational needs

Skills Intelligence Platforms

What they offer:

• Skills taxonomy management
• Gap analysis at individual and organisational levels
• Market skills data comparison
• Workforce planning insights

Evaluation criteria:

• Quality and currency of skills data
• Integration with HR systems
• Accuracy of skills inference
• Usability for different stakeholders

Administrative Tools

AI reduces administrative burden in L&D operations.

AI Assistants for Administration

What they offer:

• Automated scheduling and coordination
• Response to common learner questions
• Content recommendation to learners
• Workflow automation

Evaluation criteria:

• Accuracy of responses
• Handoff protocols when AI can’t help
• Integration with existing systems
• Time savings versus implementation effort

Content Curation Tools

What they offer:

• Aggregation of content from multiple sources
• AI-powered tagging and organisation
• Relevance recommendations
• Duplicate and outdated content identification

Evaluation criteria:

• Quality of curation algorithms
• Range of content sources supported
• Customisation for organisational taxonomy
• Governance and quality control features

Evaluation Framework

When evaluating any AI tool:

Capability Assessment

• What does it actually do well versus marketing claims?
• How does capability compare to alternatives?
• What are clear limitations and failure modes?

Integration Assessment

• How does it integrate with existing systems?
• What data flows are required?
• What’s the implementation complexity?

Quality Assessment

• What’s the accuracy and reliability?
• What happens when it fails?
• How is quality maintained over time?

Risk Assessment

• What are data privacy implications?
• What biases might exist?
• What could go wrong and how would you respond?

Value Assessment

• What’s the total cost of ownership?
• What time savings or improvements result?
• What’s the ROI calculation?

Tool Selection Strategy

Rather than chasing every new tool:

Start with problems, not tools. Identify what you’re trying to accomplish, then find tools that help.

Pilot before committing. Test tools in limited contexts before organisation-wide deployment.

Consider sustainability. Will this tool exist in two years? Is the vendor viable?

Account for learning curve. Tool value must exceed adoption and learning costs.

Think portfolio. How do tools work together? Avoid redundancy and gaps.

Tools I’m Watching

Emerging capabilities worth attention:

AI-generated adaptive learning. Systems that create personalised learning experiences automatically based on learner needs. Platforms like AI consultants Brisbane are developing adaptive approaches specifically for AI skill development.

Real-time performance support. AI that provides guidance within workflow tools as people work.

Sophisticated simulation. AI-powered practice environments for complex skills.

Automated quality assurance. AI that identifies issues in learning content before deployment.

Predictive skills forecasting. AI that anticipates future skills needs based on business signals.

These are emerging—not yet mature enough for broad recommendation, but worth watching.

The Human Element

Amid tool proliferation, remember what AI can’t do:

• Understand your organisational context deeply
• Build relationships that enable influence
• Make ethical judgments about what should be taught
• Provide authentic human connection in learning experiences
• Exercise strategic judgment about L&D priorities

AI tools amplify human capability. They don’t replace the human judgment, creativity, and relationship skills that make L&D valuable.

Use tools to do more of what matters, not to avoid the work that requires human expertise.

Getting Started

If you’re not yet using AI tools extensively:

Week 1: Start using a major LLM (ChatGPT, Claude) for content drafting. Experiment with prompts and learn its capabilities.

Month 1: Identify one administrative task AI could streamline. Implement and evaluate.

Quarter 1: Evaluate one AI-enhanced delivery or analytics tool that addresses a real need.

Year 1: Build a coherent AI tool portfolio that supports your L&D strategy.

Progress doesn’t require adopting everything. It requires adopting the right tools for your context.

Start somewhere. Learn. Expand deliberately.