Introduction
The programme “Artificial Intelligence in Designing Interactive Training Experiences” develops professional practice in Training and Learning. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from training design and measurement of learning transfer to professional communication, negotiation, and conflict management, with practical work on artificial intelligence applications and controls, performance, competency, and individual development management, data analysis and decision support. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Design a training intervention that begins with a performance gap and ends with evidence of application in an application related to “Artificial Intelligence in Designing Interactive Training Experiences”.
- Design an AI use case with verification criteria and human review.
- Build a performance agreement, feedback process, and development plan tied to specific gaps.
- Turn available data into documented analysis that supports a specific professional decision.
- Select a communication and negotiation approach that addresses interests and preserves professional relationships.
Learning areas
1. Training Design and Measurement of Learning Transfer
- Analyze performance gaps at organizational, job, and individual levels, and verify that training addresses a cause related to knowledge or skill.
- Write observable learning objectives, define entry requirements, and link each objective to a training activity and assessment evidence.
- Build a sequence of content, activities, and practical cases, balancing explanation, experimentation, and feedback within the available time.
- Select suitable learning methods, face-to-face delivery, and digital learning, and ensure accessibility, material quality, and delivery instructions.
- Design pre- and post-learning assessments and a follow-up plan with the line manager to measure application and identify additional support needs. The application examines a case related to “Artificial Intelligence in Designing Interactive Training Experiences”.
2. Artificial Intelligence Applications and Controls
- Identify a task that AI can support, and separate inputs and human decisions from outputs produced by the tool.
- Prepare suitable test data and examples while respecting confidentiality, privacy, and permissions, and use de-identified training exercise data.
- Write clear instructions covering the task, context, constraints, and output format, and compare multiple formulations using a fixed set of examples.
- Check outputs for errors, bias, and unsupported information, and identify cases that require specialist review.
- Assess the feasibility of use in terms of quality, time, cost, and risk, and document controls for approval, monitoring, and stopping when failures occur.
3. Performance, Competency, and Individual Development Management
- Translate unit objectives into agreed individual results, specifying weights, success criteria, and sources of performance evidence.
- Define competencies using observable levels and behaviors, and connect assessment to role requirements without broad personal judgments.
- Conduct a follow-up discussion covering achievements, obstacles, and required support, and record facts and commitments professionally.
- Analyze performance gaps to distinguish causes related to skills, procedures, resources, and motivation, and select an intervention suited to the cause.
- Prepare a development plan with practical tasks, support, and review dates, and monitor the transfer of learning into actual work results.
4. Data Analysis and Decision Support
- Frame an analytical question linked to a specific decision, and define the unit of measurement, time period, and data needed to answer it.
- Check data quality for completeness, accuracy, consistency, and duplication, and document cleaning steps and limitations on reliance.
- Select appropriate comparisons, indicators, and charts, and distinguish statistical correlation from causal explanations of results.
- Interpret results in the work context, test assumptions and unusual values, and present alternatives and their associated uncertainty.
- Prepare an analytical report containing the finding, recommendation, data source and date, review owner, and next follow-up step.
5. Professional Communication, Negotiation, and Conflict Management
- Analyze the communication objective, audience, context, and sensitivity, and select the channel, message, and timing to support the intended result.
- Practice listening, checking understanding, and questioning, and distinguish facts from interpretations and personal assumptions.
- Identify the parties' interests, limits, alternatives, and comparison criteria, and prepare a list of negotiable points.
- Handle objections and disagreements by reframing the problem, generating alternatives, and documenting agreements.
- Simulate a communication or negotiation situation, assess message clarity and response quality, and plan improvements for subsequent practice.
Practical outputs
- A needs analysis, concise learning package, and application evaluation plan.
- A use case brief and a test template for the quality of AI outputs.
- A performance agreement card, individual development plan, and review session guide.
- A dashboard and decision memo explaining the data, assumptions, and alternatives.
- A communication plan, interests matrix, and actionable negotiation scenario.
Who should attend
- Practitioners, specialists and supervisors whose work relates to the programme subject.
- Managers and teams working in training and learning, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Artificial Intelligence in Designing Interactive Training Experiences” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Artificial Intelligence in Designing Interactive Training Experiences” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.