Problem

Learner Survey

RESULT

AI-Driven Learner Profiling System

Bridging data and design—using AI to understand learners and build customized learning experiences.

Tools:

Google Survey, Google Sheet, GPT workspace

Audience: Instructional Designer, SMEs, Stakeholders

The task is to develop a technology workshop for DTA clients, who come from diverse educational, linguistic, and technological backgrounds. This wide range of learner profiles makes the analysis phase both complex and time-intensive. Accurately understanding each client’s needs is essential to delivering a tailored and effective learning experience.

To address this challenge, this project integrates AI as a powerful tool to assist in analyzing learner data. By automating the creation of individualized learning profiles, AI reduces the manual workload and helps uncover meaningful insights—enabling the design of inclusive, targeted, and high-impact technology workshops.

To address the challenge of designing instruction for a diverse learner population, a structured learner survey was developed to collect essential background information from participants. The survey gathers data on:

  • Preferred language

  • English proficiency level

  • Age

  • Computer/technology skill level

  • Access to digital devices

  • Learning preferences, including:

    • In-person vs. online learning

    • Demonstration vs. hands-on activities

    • Individual vs. group work

      etc.

Data Visualization & Stakeholder Discussion

As the next step, I transformed the survey results into clear, visual charts to present directly to stakeholders. This visual representation of the data allows for a more intuitive understanding of the learners’ backgrounds and preferences.

By reviewing these graphs together, we can quickly identify patterns—such as the most common preferred learning styles, language needs, and technology access—and make informed decisions about the instructional strategies to use. This step ensures that the final workshop design is data-driven, transparent, and aligned with the actual needs of our learners.

AI Integration in Analysis

To further streamline the analysis phase and enhance instructional planning, I integrated an AI tool directly into Google Sheets using the ChatGPT Workspace add-on. After collecting survey responses, I used this AI integration to:

  • Analyze learner data automatically

  • Identify key patterns and trends

  • Generate instructional design suggestions tailored to the group’s overall profile

individualized learner profiles

The AI-generated insights help identify learners who may require special attention—for example, due to their age, preferred language, English proficiency, or learning preferences. These profiles help instructors proactively adjust their approach, such as:

  • Providing extra translation support

  • Choosing more age-appropriate examples

  • Modifying group formation strategies for teamwork activities

  • Offering additional one-on-one guidance when needed

This targeted approach ensures that the workshop is not only effective for the majority but also inclusive and responsive to individual learner needs.

this AI-supported approach allowed instructional designers to focus more on strategic decision-making and course design—while still gaining deep, data-driven insights into learners’ needs, backgrounds, and preferences.

In workshops with similar learner backgrounds and contexts, this approach achieved a 97% satisfaction rate and an 87% learner return rate, demonstrating its strong impact on improving both instructional efficiency and learner experience.