Practical and responsible ways to use AI in professional development design
Professional development (PD) is one of the first terms many of us learn when entering the world of work. PD can cause a wide range of emotions, from excitement and curiosity to apathy and fear. Regardless of how we feel about it, PD is here to stay. AI is rapidly transforming both daily life and the workplace. As organizations develop targeted PD around AI and its use, many people who are already proficient with AI are beginning to realize that AI can improve their efficiency and, in some cases, the quality of their contributions. At the same time, educators, trainers, and PD leaders face increasing pressure to design high-quality, flexible professional learning.
Although AI is sometimes met with fear, including concerns about job displacement and inaccurate output, its potential to improve productivity and personalize learning also inspires optimism. PD is uniquely positioned to help employees develop the knowledge, skills, and mindset needed to use AI ethically and effectively. For PD designers and leaders, AI offers many benefits. Using AI as your digital thinking partner can accelerate content creation and reduce editing and formatting workloads. This article outlines five practical ways PD designers can responsibly use AI to create meaningful and scalable learning experiences.
Understanding LLM in Professional Development Design
Large Language Model (LLM) is an AI tool that can understand and generate human-like language. Trained on vast amounts of text and code, they identify patterns and predict what will happen next in a sequence. Common concerns include:
Output bias. Illusions or inaccuracies. Overreliance can reduce a user’s critical thinking. Privacy, especially regarding personal or organizational data.
Despite these concerns, many professionals find that AI can improve their work when used judiciously. In PD design, LLMs should be treated as supporting tools rather than autonomous authors. Ultimately, human designers remain responsible for the quality, accuracy, and appropriateness of the PD they develop, with or without AI. Here are five practical ways to use AI to design PD and training.
1. Generate and improve learning outcomes in line with set goals
Effective PD design begins with best practices such as ADDIE, Bloom’s Taxonomy, SAM Model, Dick and Carey Model, or Kemp Design Model. Fidelity to existing standards, performance metrics, or organizational goals is essential, and these elements must remain central to the PD structure. LLM helps you draft, revise, or differentiate learning outcomes for multiple roles or experience levels, supporting greater personalization and relevancy. Once outcomes are established, AI can also help generate evaluation and tailored activities (see item 4). While AI can support the design process, it is important to note that it should not replace teaching expertise or determine learning paths.
2. Summarize important texts and resources to support presentation and material development
PD should be based on current research, organizational priorities, and professional expectations. AI can summarize important text, resources, or notes from subject matter experts (SMEs) so designers can quickly create slide decks, facilitator guides, or resource packets. Always check for accuracy and cite sources. The summary is a starting point and is not a substitute for understanding.
3. Drafting or revising rubrics, scoring and feedback criteria.
AI can help develop rubrics and feedback criteria that align with learning outcomes and performance metrics. LLMs can identify key assessment criteria and suggest rubric categories, achievement levels (e.g., developing, proficient, excellent), and weighting considerations. This is particularly useful for open-ended tasks, reflections, or performance-based demonstrations that naturally vary from participant to participant. AI can also help create feedback templates and sentence stems that instructors can customize.
When PD involves multiple roles, such as organization-wide onboarding or training for teachers, paraprofessionals, faculty, and administrators, rubrics provide consistent expectations across diverse participant groups. Because the design is iterative, especially when using backward design, revisiting the rubric as the course evolves is expected and beneficial.
4. Development and improvement of instructional activities
LLMs can support the creation of engaging and tailored teaching activities. Designers can use AI to generate or adjust:
Multiple choice, true/false, and short answer questions. Discussion prompts. Case study or scenario. Workplace simulation or role-play scripts. Datasets created for data literacy practices that include Excel or visualization. Creating prompts. Reflective prompts or diary questions. Text and images for interactive digital tools (H5P branching scenarios, drag-and-drop activities, gap fill, etc.) Work aids, checklists, or quick reference guides. An overview of your microlearning module or lesson. important
Designers remain responsible for ensuring that all materials are accurate, culturally responsive, accessible, and appropriate for their audience.
5. Provide examples of completed tasks, assignments, or reflections
Models help participants understand expectations before they begin. After providing assignment instructions and criteria, the LLM can draft sample responses that demonstrate various performance levels. For best results, include parameters such as:
focus or topic. Target level or score. Strengths that can be demonstrated. Explains common errors and weaknesses.
These examples support clarity, reduce confusion, and improve consistency between results and evaluations.
Considerations for responsible use of AI in professional development design
AI-powered design is iterative. It is expected that you will revise elements as you build, test, and refine your PD module. Designers must:
Stay flexible. Predict the unknowns of AI-assisted creation. Approach the design process with curiosity and openness. Treat AI as a collaborator, not an expert or a shortcut.
AI should support human creativity, not replace it.
remain human-centered
AI can expand the capabilities of PD designers, school leaders, instructional designers, and L&D professionals. When used thoughtfully, LLM streamlines planning, generates rich educational materials, and supports scalable PD models while keeping the human element front and center. The goal is not to automate professional learning, but to increase the creativity, clarity, and intentionality of the humans who design it.
