The Motivated Strategies for Learning Questionnaire (MSLQ) is a comprehensive tool developed in 1991 by a team including Paul R. Pintrich, David A. F. Smith, Teresa Garcia, and Wilbert J. McKeachie. Spanning a five-year period, they distilled 81 questions into 15 scales, measuring aspects such as motivation, study strategies, and cognitive habits. However, accessing this treasure trove of educational insights often requires navigating a grainy, scanned PDF marked “BEST COPY AVAILABLE,” available from the Education Resources Information Center (ERIC). Resurrecting the MSLQ An enthusiast of educational technology, I aimed to revive the MSLQ to gain deeper insights into personal learning behaviors and support colleagues. The process involved several steps: Extraction and Formatting: I initially attempted to answer the questions manually but quickly realized this approach was inefficient. Using Claude, an advanced AI model, I leveraged Optical Character Recognition (OCR) to extract the 81 questions. Claude generated an Excel sheet template where users can input their responses as numbers, and the 9 scales—each linked to specific questions—are automatically calculated. The Excel includes three worksheets: Questions, Results, and Metadata. The Questions tab lists all 81 questions with a description, while the Results tab displays the scores for each scale, and the Metadata tab provides additional context. Technical Implementation: The Python code to create the Excel template was robust, generating nearly perfect output in one go. It included complex formulas for AVERAGE, COUNTIF, IF, and cross-sheet references, ensuring the scales were accurately computed. To improve user experience, I removed the scale and reverse-coded statuses from the Questions tab to prevent biasing responses and added clear instructions at the top. The template also includes a command-line interface (CLI) for customizing the output path and respondent count, though I later hardcoded these for speed. Validation: Ensuring the accuracy of the extracted data was crucial. I asked Claude to highlight the PDF with the exact locations of each question, reverse-coded status, and scale association, akin to back-translation in linguistics. The resulting highlighted PDF, along with a validation report, indicated that 77 out of 81 questions were found, and 8 reverse-coded items were correctly identified. However, 4 questions remained missing, primarily due to slight differences in wording or formatting. Debugging and Improvements: For the missing questions, I manually adjusted the code to account for additional words or formatting differences. This improved the accuracy of the extraction. I ensured that the Excel template could still function seamlessly, even with the adjustments. The final product is a reliable tool for analyzing motivational and cognitive strategies in learning. Personal Reflection Completing the MSLQ questionnaire reflected on my university experiences, providing valuable insights into my learning behaviors and motivations: Effort Regulation: My high effort regulation score indicates a strong ability to set and achieve goals when motivated. The real challenge lies in setting meaningful goals and avoiding burnout. Metacognitive Self-Regulation: An average score here suggests a balanced approach to planning and monitoring study sessions. Over-planning during my early years led to a draining lack of inspiration and spontaneity. Leadership roles in organizations like AIESEC required significant time commitment, impacting my grades but enriching my life skills. Elaboration: My average elaboration score reflects limited note-taking and deep understanding due to the intense pace of studying two degrees in four years. Time constraints often forced me to rush through materials at high speeds. Rehearsal: A low rehearsal score aligns with my aversion to rote memorization. I preferred higher-order thinking and connecting ideas, exploring memory techniques occasionally but not committing to them extensively. Moving Forward Reflecting on these scores, I plan to harness my high-performance mode responsibly, perhaps a few times a year. Post-university, I have more control over the work I invest in, reducing the need for strict effort regulation. Instead, I aim to sharpen my metacognitive self-regulation, focusing on effective planning, adaptability, and comfort with uncertainty. Industry Insights and Evaluation Resurrecting the MSLQ through AI highlights the potential for transforming forgotten research into practical tools. Industry insiders praise the approach, noting that integrating historical educational models with modern technology can bridge gaps in current educational practices. Philip Guo’s Computational Pedagogy Research underscores the importance of leveraging AI to enhance educational frameworks and tools. Company Profiles: - ProQuest: Provides extensive databases and resources for educational research. - Claude: An AI model known for its efficiency and accuracy in tasks such as OCR and data extraction. This project demonstrates that with the right tools and methodologies, we can not only generate the future but also reclaim valuable insights from the past, empowering communities, schools, and workplaces.