Needs Assessment: Learning Ecosystem Integration Framework (LEIF)

Context and Problem Description

In both corporate and higher education environments, learning ecosystems are constrained by legacy systems that emphasize compliance, content distribution, and efficiency rather than adaptability, reflection, or demonstrable learner growth. These environments often prioritize outcomes that are easy to measure, such as completion rates, over those that matter most, like skill transfer, ethical reasoning, and innovation. As a result, instructional design practices risk stagnation at a time when the workforce is demanding agility, creativity, and continuous learning.

At the same time, artificial intelligence (AI) has rapidly transformed the landscape of education and professional learning. Tools that can personalize learning, analyze engagement data, and streamline content creation are widely available, yet many educators and instructional designers remain hesitant or unequipped to use them effectively. This hesitation often stems from ethical uncertainty, lack of institutional guidance, and insufficient training in data-informed instructional strategies.

Educators and designers also face a widening disconnect between consumer-grade learning experiences (like Duolingo, Coursera, and YouTube Learning) and institutional training systems. Learners are accustomed to seamless, personalized digital interactions outside of formal education but encounter rigid and outdated experiences inside formal learning environments. This discrepancy not only impacts motivation but also reduces the credibility and impact of professional learning programs.

The Learning Ecosystem Integration Framework (LEIF) directly addresses these challenges. It offers a strategic model for integrating AI technologies, Universal Design for Learning (UDL), and experiential learning theory into unified digital ecosystems that promote ethical practice, learner autonomy, and continuous reflection. LEIF provides instructional designers and learning leaders with a blueprint for creating courses that are adaptive, inclusive, and sustainable across organizational contexts.

Needs and Task Analysis Plan

The needs analysis identified a core learning audience of adult professionals working in instructional design, higher education, and corporate learning and development. These learners typically possess strong pedagogical expertise but show varying degrees of confidence with data-driven decision-making and AI-enhanced learning design. The analysis revealed that while most participants recognize the potential of AI, few have structured methods for integrating it ethically or effectively into learning environments.

The task analysis involved mapping the skills, knowledge, and dispositions required to implement LEIF. Essential competencies include the ability to: Identify inefficiencies or gaps within existing digital ecosystems. Apply ethical frameworks to evaluate and adopt emerging technologies. Align AI tools and data analytics with learner-centered outcomes. Use reflective practice to iteratively improve instructional design.

Data collection for this analysis will combine both qualitative and quantitative methods. Surveys will measure learners’ self-reported confidence in using AI and data tools; stakeholder interviews will gather insights on institutional constraints; and learning management system (LMS) analytics will help identify patterns in engagement, participation, and assessment completion. Together, these data sources will inform the design of a professional learning module that models ethical, evidence-based, and learner-centered practices using the LEIF framework.

Proposed Solution

The proposed solution is a digital professional learning module based on the Learning Ecosystem Integration Framework. This module will be developed in Articulate, an authoring tool that supports responsive design and accessibility across devices. To promote collaboration and experiential engagement, the module will integrate several complementary platforms (I am not locked in to these tools, please help if you know of others that would be better!):

  • Miro for visual ecosystem mapping and collaborative design.

  • Padlet for reflective journaling and peer dialogue.

  • Hypothes.is for social reading and annotation of scholarly texts.

  • Flip for multimodal reflective storytelling through video responses.

Instructionally, the LEIF module will be grounded in Kolb’s Experiential Learning Cycle, guiding participants through iterative stages of experience, reflection, conceptualization, and application. Backward Design will ensure coherence between objectives, instructional activities, and assessments, while UDL principles will ensure that all content is perceivable, operable, and engaging for diverse learners.

Learners will begin by analyzing real-world case studies that illustrate ethical dilemmas in AI integration. They will then apply LEIF principles to create their own ecosystem prototypes, mapping the connections among learners, tools, data, and outcomes. The final deliverable, a reflective design portfolio, will demonstrate their ability to align emerging technologies with pedagogical integrity and measurable impact.

Implementation Strategy

Implementation will occur over an eight-week cycle that mirrors an agile design process, allowing for ongoing feedback and iteration.

Weeks 1–2 will focus on refining the needs assessment and synthesizing existing data into learner personas and performance goals. Weeks 3–4 will move into storyboard development, establishing clear learning outcomes and selecting digital tools aligned to each stage of the LEIF cycle. Weeks 5–6 will involve full content development within Articulate, integration of Miro and Padlet activities, and accessibility testing. Week 7 will be dedicated to a pilot test with six to ten professionals, whose feedback will inform final refinements before the full launch in Week 8.

The module will be delivered asynchronously to accommodate working professionals but will feature structured interaction points, such as peer feedback, discussion prompts, and instructor video reflections, to maintain social presence. The instructor will provide personalized support via Loom video check-ins and formative feedback on design drafts. This structure combines flexibility with accountability, creating a rich, community-oriented learning experience.

Assessment Method

Evaluation of learning will be multifaceted, combining formative and summative measures aligned with the LEIF framework. Formative assessments will include reflective journals, discussion posts, and early design drafts that allow for peer and instructor feedback. Summative assessments will center on a comprehensive LEIF ecosystem prototype and an accompanying reflective video analysis that connects theory to practice.

Success will be evaluated across several dimensions. Post-module surveys will measure learner satisfaction, with a target of at least 90% positive feedback. Platform analytics within Articulate and Miro will track engagement, aiming for at least 75% active participation. Mastery will be determined through a rubric evaluating conceptual understanding, ethical reasoning, and design creativity, with a target threshold of 85% proficiency.

The Kirkpatrick Model (Levels 1–3) will guide overall evaluation, assessing reaction, learning, and behavior, to determine both the effectiveness of the instructional design and its real-world transferability. Long-term follow-up surveys may also be implemented to assess continued application of LEIF principles in professional practice.

Ultimately, success will be defined not only by knowledge gains but by learners’ ability to design adaptive, inclusive, and ethically grounded learning ecosystems that bridge the gap between traditional instruction and emerging technology.

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.