Artificial Intelligence is transforming how we define learning and cognition. This session introduces the MetaBloom Framework (BloomAI Model), reinterpreting Bloom’s Taxonomy for AI-rich classrooms. Participants will explore how to design, assess, and ethically integrate human–AI co-creation to foster higher-order thinking and authentic learning.

Overview
Artificial Intelligence (AI) has redefined what it means to remember, analyze, and create. Frameworks such as Bloom’s Taxonomy have long guided educators in structuring cognitive outcomes, yet they were never built for contexts where machines can retrieve, evaluate, and generate knowledge artifacts.

This session introduces the MetaBloom Framework (BloomAI Model)—a theoretical and practical model that extends Bloom’s taxonomy across three modes of cognition:

Human-Centered Cognition – traditional independent cognitive performance.

AI-Augmented Cognition – learners direct and evaluate AI outputs.

Human–AI Co-Creation – learners collaborate with AI to produce original, ethical, and contextualized work.

MetaBloom reframes higher-order thinking as meta-cognition, orchestration, and ethical engagement. It equips educators and instructional designers to update learning outcomes, redesign assessments, and embed AI literacy into their courses.

Session Purpose and Alignment with Conference Theme
Under the OLC Innovate 2026 theme “Future in Focus: Vision, Innovation, and Global Impact for a New Era,” this session demonstrates how instructional design must evolve as cognitive work becomes distributed across humans and intelligent systems. The MetaBloom Framework offers a forward-thinking lens for course and program design that maintains rigor, transparency, and learner agency in the age of AI.

Session Structure (45-minute Education Session)
0–5 min | Opening Context
• Live poll: “Which Bloom’s level do you think AI performs best?”
• Overview of Bloom’s legacy and why AI disrupts its human-centered assumptions.

5–15 min | Conceptual Framework
• Presentation introducing the MetaBloom model.
• Visual pyramid and table show how Bloom’s six levels map across three modes: human, AI-augmented, and human–AI co-created.
• Discussion of meta-cognition and ethical orchestration as the new apex of higher-order learning.

15–25 min | Design in Practice
• Real-world examples of learning objectives rewritten using MetaBloom (e.g., “Students will analyze data independently and evaluate AI-generated analyses for bias and accuracy”).
• Demonstration of process-based assessment: evaluating how learners use and critique AI, not just what they submit.

25–35 min | Engagement Activity
• Collaborative Design Sprint: Participants select one of their own course objectives and redesign it using the MetaBloom template.
• Share-outs via Jamboard or shared doc to discuss design choices and challenges.

35–40 min | Reflection and Discussion
• Group reflection: “Where does your institution stand on AI as a cognitive partner?”
• Discussion on academic integrity, transparency, and equity in AI-supported learning.

40–45 min | Takeaways and Q&A
• Recap of key takeaways.
• Distribution of resources: MetaBloom Quick-Reference Guide, editable rubric, and “Modes of Cognition” outcome template.

Active Audience Engagement
Participants will respond to live polls, collaborate in a design sprint, and reflect through shared discussion boards. Interactive tools (Mentimeter, Padlet, or Jamboard) will ensure inclusive engagement for all attendees, including virtual participants. Everyone leaves with practical tools to apply MetaBloom immediately in their own teaching contexts.

Learning Outcomes
By the end of this session, participants will be able to:

Explain how AI challenges traditional interpretations of Bloom’s Taxonomy.

Apply the MetaBloom Framework to design outcomes that specify cognitive mode (human, AI-augmented, or co-created).

Develop authentic, process-based assessments that evaluate learner orchestration of AI.

Integrate AI literacy and ethical reasoning into teaching and instructional design practice.

Intended Audience
Primary: Instructional Designers, Faculty Developers, and Educators in Higher Education.
Secondary: Academic Leaders, Assessment Specialists, and Technologists interested in aligning policy and practice for AI-enabled learning.
Participants need general familiarity with Bloom’s Taxonomy but no prior AI experience.

Special Session Designations (select all that apply)
☑ Instructional Designers – Central focus on design frameworks.
☑ Leaders and Administrators – Addresses institutional strategy, ethics, and assessment redesign.
☑ Original Research – Introduces a novel theoretical model (MetaBloom Framework).
☑ Open Education – Emphasizes transparency and ethical co-creation with AI.

Relevance and Impact
MetaBloom fills a critical gap in education: traditional taxonomies assume cognition is human and linear, but AI introduces distributed, co-constructed cognition. The framework helps preserve cognitive rigor while adapting to emerging realities. It provides a shared language for policy, design, and assessment in AI-pervasive environments—ensuring institutions remain human-centered while leveraging technology responsibly.

This work builds on Bloom’s Taxonomy (Bloom et al., 1956; Anderson & Krathwohl, 2001), distributed cognition (Hutchins, 1995), and AI literacy research (Long & Magerko, 2020; Ng, 2021). It stands at the intersection of cognitive theory, educational design, and technological ethics.

Takeaways and Resources
• MetaBloom Framework infographic (PDF).
• Editable “AI-Aware Learning Outcomes” template.
• Rubric for assessing human–AI orchestration.
• Reading list on AI literacy and ethics in instructional design.

Conclusion
As AI reshapes how knowledge is created and evaluated, educators must update not only their tools but their cognitive frameworks. The MetaBloom Framework bridges Bloom’s enduring clarity with the realities of AI-augmented learning. By reinterpreting higher-order thinking as meta-cognition, orchestration, and ethical integration, this model prepares instructional designers and faculty to lead education into its next era—where learners are not merely users of AI, but orchestrators of distributed intelligence.

Dr. Yassine Rfissa

Instructional Designer at University of Nebraska System Office

Dr. Yassine Rfissa is an Instructional Designer at the University of Nebraska Omaha and a lecturer in linguistics at the University of California, Berkeley. He holds a Ph.D. in Educational Studies, with research focused on the intersection of cognitive theory, artificial intelligence, and instructional innovation. His professional work centers on online learning design, meta-cognition, and authentic assessment. Dr. Yassine develops faculty development programs and learning experiences that promote ethical AI integration, learner agency, and inclusive, human-centered pedagogy.