Learning Ecosystem Integration Framework (LEIF): Instructional Design Proposal

Hannah Shambley
Learning Experience Designer (LxD)
Designing adaptive, ethical, and experiential learning ecosystems

Executive Summary

The Learning Ecosystem Integration Framework (LEIF) represents a forward-thinking instructional design model that bridges theory, technology, and ethical practice. This proposal outlines the design and implementation of a professional learning module based on LEIF principles, addressing the growing demand for adaptive, data-informed, and inclusive learning ecosystems. The project targets adult professionals in higher education and corporate learning who seek to ethically integrate artificial intelligence (AI) and Universal Design for Learning (UDL) into their instructional design practice.

The LEIF module operationalizes experiential learning theory, accessibility, and AI literacy within a single coherent system. It provides a scalable model for developing reflective, ethical, and future-ready instructional design practices across industries.

Context & Problem Statement

Corporate and academic learning environments continue to rely on outdated systems that prioritize compliance and efficiency over reflection, adaptability, and measurable learning outcomes. These static structures limit learner engagement, data-informed iteration, and authentic performance outcomes.

Meanwhile, rapid advancements in artificial intelligence and adaptive learning tools have outpaced most educators’ and designers’ ability to apply them effectively. Many institutions lack clear policies or frameworks for integrating AI responsibly. As a result, instructional designers face uncertainty about how to leverage these tools without compromising privacy, equity, or pedagogical integrity.

The Learning Ecosystem Integration Framework (LEIF) addresses this challenge by offering a structured approach that aligns human-centered design, UDL principles, and experiential learning theory with AI-enhanced technology. It provides a replicable model for creating learning ecosystems that are reflective, ethical, and adaptive to learner needs.

Needs & Task Analysis

A mixed-methods needs analysis identified adult instructional designers, faculty developers, and corporate L&D professionals as the target audience. These learners exhibit varying levels of technical proficiency, data literacy, and familiarity with ethical design practices.

Findings revealed key needs:

  • A cohesive framework for integrating AI and emerging tools into instructional design.

  • Stronger grounding in ethical decision-making related to data and technology.

  • Improved understanding of adaptive learning design and UDL-driven accessibility.

The task analysis outlined essential learner competencies, including:

  1. Identifying system inefficiencies and opportunities for adaptive improvement.

  2. Applying ethical frameworks to evaluate emerging learning technologies.

  3. Mapping interconnected systems of learners, tools, and data.

  4. Using reflection and iteration to improve instructional outcomes.

Data collection methods include stakeholder interviews, learner surveys, and learning management system (LMS) analytics. The results inform both the instructional objectives and the content sequence of the LEIF module.

Instructional Solution (LEIF Model)

The LEIF learning module will be developed in Articulate Rise for accessibility, interactivity, and responsive design. Supporting tools include:

  • Miro for collaborative ecosystem mapping.

  • Padlet for reflection and peer discussion.

  • Hypothes.is for social reading and annotation.

  • Flip for multimodal reflective storytelling.

The design integrates Kolb’s Experiential Learning Cycle (experience → reflection → conceptualization → application) and Wiggins & McTighe’s Backward Design, ensuring that learning outcomes, assessments, and activities remain tightly aligned. Universal Design for Learning (UDL) principles ensure accessibility and flexibility, offering multiple means of engagement, representation, and expression.

Learners will engage with interactive case studies exploring ethical AI integration and adaptive design. They will build their own prototype ecosystems, connecting theory to applied practice through reflection and collaboration.

Learning Objectives

By the end of this module, learners will be able to:

  1. Explain the foundational principles of the Learning Ecosystem Integration Framework (LEIF).

  2. Analyze ethical considerations of AI integration within instructional design.

  3. Design an adaptive, inclusive learning ecosystem prototype aligned with LEIF principles.

  4. Evaluate the effectiveness of an ecosystem design through reflection and data-informed decision-making.

Instructional Strategies & Media

The LEIF module employs active and experiential learning strategies emphasizing autonomy, collaboration, and reflective practice. Learners interact with multiple digital tools that simulate authentic professional design challenges.

  • Articulate Rise: Core structure and module navigation.

  • Miro: Visual collaboration for ecosystem mapping.

  • Padlet: Reflection boards for peer dialogue and sharing.

  • Hypothes.is: Collaborative annotation to support critical reading.

  • Flip: Multimodal expression for reflective storytelling.

These tools support andragogical principles by valuing prior experience, encouraging self-directed exploration, and connecting content to authentic professional practice.

Accessibility & Inclusion

Accessibility and inclusion are foundational to the LEIF model. The design adheres to WCAG 2.1 and UDL 2.2 guidelines to ensure equitable access for all learners. Key accessibility features include:

  • Closed captioning and transcripts for all media.

  • Alt text and keyboard navigation for visual and interactive content.

  • Multiple modes of representation (visual, textual, auditory).

  • Adjustable pacing and flexible engagement formats.

Accessibility is treated not as an accommodation but as a proactive design decision embedded in every stage of development.

Implementation Timeline

The project will follow an eight-week agile development cycle:

  • Weeks 1–2: Conduct needs assessment, stakeholder interviews, and finalize learning objectives.

  • Weeks 3–4: Storyboard the module, align assessments, and integrate selected tools.

  • Weeks 5–6: Build and test content in Articulate Rise; configure collaborative tools.

  • Week 7: Pilot test with 6–10 professionals; collect data and qualitative feedback.

  • Week 8: Revise, finalize, and launch the full LEIF module for broader use.

Delivery will be asynchronous to accommodate adult professionals but will include structured touchpoints through video reflections and peer discussions to maintain community and accountability.

Evaluation & Assessment Plan

Evaluation will be guided by Kirkpatrick’s Model (Levels 1–3)—assessing reaction, learning, and behavioral change.

Formative Assessments:

  • Reflective journals and Padlet discussions.

  • Draft ecosystem prototypes reviewed by peers and instructor.

Summative Assessments:

  • Final LEIF ecosystem design project.

  • Reflective video connecting theory to professional practice.

Success Metrics:

  • Learner satisfaction ≥ 90%.

  • Engagement ≥ 75%.

  • Mastery (final project score) ≥ 85%.

Data from analytics and surveys will be used for continuous improvement and long-term scalability.

Anticipated Outcomes & Sustainability

The LEIF model will empower learners to integrate AI ethically, design for accessibility, and build adaptive learning experiences that reflect modern workplace demands. It will serve as a replicable framework for instructional teams seeking to future-proof professional learning initiatives.

The module’s modular and tool-agnostic design ensures it can be updated continuously as technologies evolve. Future iterations may include micro-credentialing, analytics dashboards, and advanced AI-driven simulations to expand its scalability and institutional value.

References

CAST. (2023). Universal Design for Learning Guidelines 2.2.
Kolb, D. A. (2015). Experiential learning: Experience as the source of learning and development. Pearson.
OpenAI. (2024). ChatGPT app ecosystem announcement.
Wiggins, G., & McTighe, J. (2005). Understanding by design. ASCD.
Coursera. (2024). AI learning integration report.