Using AIEd Bloom’s Taxonomy to Design Effective Micro PD Sessions

The third in a new series of expert articles from Dr Sheena Bahra for the Digital Learning Institute.

A well‑designed micro‑PD session is structured around participants demonstrating their ability to achieve a clearly defined outcome by the end of the learning experience. Applying a learning taxonomy to a microlearning PD session provides a strong framework for assessing learners’ ability to meet the desired outcomes related to skills and knowledge. A range of Bloom’s‑based taxonomy models are available to support learning designers and educators. These include the revised Bloom’s Taxonomy (Anderson & Krathwohl), the Digital Bloom’s Taxonomy (Churches) developed for technology‑enhanced and online learning environments, and the emerging AIED Bloom’s Taxonomy designed specifically for artificial intelligence in education.

In this article, Dr Sheena Bahra explores a newly proposed AIED Bloom’s Taxonomy model developed by Hmoud & Ail (2024). Drawing on her EdD research, GenAI Professional Development Program for Higher Education Instructors, she positions this model as a useful integration framework for professors designing learning that incorporate GenAI tools, pedagogical strategies, and learning activities. This can also be used by learning and development professionals.

This work matters because AI is advancing rapidly, and organisations and educational institutions are increasingly developing training and courses that include GenAI applications, activities, or learning tasks. Designing a session using a taxonomy that aligns with the digital tools students will use, the learning activities they will engage in, and the assessments they will complete is essential for supporting participant learning and growth. A misalignment between taxonomy, tools, and learning design can lead to missed opportunities for development and hinder learners’ ability to progress effectively.

The AIED Bloom’s Taxonomy proposes a new proposed taxonomy that aligns with the evolving landscape of artificial intelligence in education. Its taxonomy levels have been adapted to reflect how learners engage with AI tools, progressing from lower‑order to higher‑order abilities within an AI‑supported learning model. Hmoud & Ail (2024) outline the AIED Bloom’s Taxonomy as consisting of the following levels:

• Collect: Using AI tools to support research activities by gathering, organising, and synthesising information relevant to the learning context.

• Adapt: Applying AI tools to design and shape learning experiences that respond to learners’ needs and contexts.

• Simulate: Integrating AI simulation tools to create authentic learning experiences that mirror real‑world practice.

• Process: Employing AI technologies to interpret and analyse data, enabling deeper understanding and informed decision‑making.

• Evaluate: Using AI tools to help learners reflect on and assess their progress, identifying areas for growth and improvement.

• Innovate: Engaging with advanced AI tools to explore new ideas, generate creative solutions, and extend research and practice.

Example: Integrating AIEd Bloom’s Taxonomy in L&D

In this example, LD staff begin by using Claude to collect relevant information and organisational content. They then adapt Claude’s outputs to shape learning materials that suit their context. As confidence grows, staff simulate real workplace scenarios using Claude to practise decision‑making and communication. They move on to processing data, using Claude to interpret feedback and generate insights. Staff then evaluate their own learning by reviewing Claude’s outputs and refining their prompting strategies. Finally, they reach the innovate stage, where they design new AI‑enhanced approaches to communication, and workflow improvement.

There are several advantages to applying AIEd Bloom’s Taxonomy when designing micro‑professional development sessions:

• Alignment of tools and outcomes: It helps align the chosen digital tool with the learning activity and ensures that each task leads to a clearly defined outcome.

• Learner‑centred design: It makes the session more participant‑focused, encouraging active engagement and reflection rather than passive consumption.

• Structured microlearning: It supports the division of content into 10 to 15‑minute segments, ideal for microlearning and sustained learning attention.

• Progressive skill development: Each level builds on the previous one, guiding learners from foundational understanding to innovative application of AI tools.

• Consistency in evaluation: It provides a consistent framework for assessing learning outcomes and measuring growth across different PD sessions.

When applying the AIEd Bloom’s Taxonomy for the first time, begin with a clear focus on one level of the taxonomy that aligns with your session’s learning goal and the AI tool being introduced. This helps participants concentrate on developing a specific skill before progressing to higher levels of cognitive engagement.

• Select one taxonomy level: Identify the level most relevant to your session outcomes. For example, ‘Process’ for data analysis or ‘Evaluate’ for prompt review.

• Design purposeful activities: Choose an activity that encourages participants to actively engage with the AI tool rather than observe passively.

• Match the AI tool to the learning goal: Select a tool that supports the intended skill development. Remember, whichever tool is used becomes the focus of participants’ skill growth.

• Check microlearning suitability: Confirm that both the AI tool and the activity can be completed within a 10 to15-minute microlearning window. Activities should be short, focused, and produce a clear output that aligns with the taxonomy level.

• Plan assessment strategies: Decide how you will measure learning through reflection prompts, generated outputs, or peer feedback.

• Provide tool orientation: Ensure participants receive basic training or guidance on the AI tool before the main activity begins.

• Evaluate and reflect: Review the session’s effectiveness at the end, gathering feedback to refine future micro‑PD designs.

• Encourage ethical awareness: Discuss responsible AI use, data privacy, and bias awareness as part of the learning process.

• Integrate progression: Over time, design sessions that move participants through multiple taxonomy levels from Collect to Innovate.

The AIEd Bloom’s Taxonomy is a proposed framework that supports the design of microlearning professional development sessions by structuring how AI tools are integrated into learning. It offers educators and L&D professionals a clear progression of cognitive levels through which participants can develop their AI skills, ensuring that activities and tools are purposefully aligned with the intended learning outcomes. Because the model is designed specifically for AI‑supported learning, it provides opportunities for participants to engage with AI tools across several levels of taxonomy‑based abilities from foundational tasks to more advanced learning. When using AIEd Bloom’s Taxonomy, it is essential to consider the alignment between the chosen AI tool, the microlearning activity, the time constraints, and the level of the taxonomy being targeted to ensure an effective PD experience.

What is AIEd Bloom's Taxonomy?
AIEd Bloom's Taxonomy is a proposed model by Hmoud & Shaqour (2024) that adapts Bloom's Taxonomy for AI-supported learning. It outlines six levels — Collect, Adapt, Simulate, Process, Evaluate, and Innovate — that reflect how learners progress from foundational to advanced use of AI tools.

How is AIEd Bloom's Taxonomy different from traditional Bloom's Taxonomy?
While traditional and digital versions of Bloom's Taxonomy focus on general or technology-enhanced learning, AIEd Bloom's Taxonomy is designed specifically around how learners engage with AI tools, progressing from collecting and adapting information to simulating, processing, evaluating, and ultimately innovating with AI.

How can AIEd Bloom's Taxonomy be used in micro-PD design?
It can be used to align a chosen AI tool, learning activity, and outcome to a specific taxonomy level — for example, focusing a 10–15 minute microlearning session on just the "Process" or "Evaluate" level before progressing participants to higher levels over time.

What are the benefits of using AIEd Bloom's Taxonomy in professional development?
It helps align digital tools with learning outcomes, keeps sessions learner-centred, supports structured microlearning in short segments, builds progressive skill development, and provides a consistent framework for evaluating learning across PD sessions.

What should you consider before using AIEd Bloom's Taxonomy in a session?
It's important to select one taxonomy level to focus on, match the AI tool to the learning goal, confirm the activity fits a microlearning time window, plan how you'll assess learning, and build in ethical awareness around responsible AI use.