2024 ASEE Annual Conference & Exposition

WIP. The demand for high-quality online engineering courses and credentials is surging, driven by the upskilling and reskilling needs of industry partners and engineers with 8-10 years of experience. Creating accessible, top-tier online courses require producing exceptional videos, transcripts, and content segmented appropriately for optimal student learning[1]. Beyond lecture preparation, faculty are often tasked with creating well designed slides and assessments to engage students and measure learning. Despite the support of instructional designers in many institutions, this multifaceted process presents a significant challenge for faculty, who are often concurrently engaged in research, service duties, and mentoring activities [2], [3].
To support instructional designers and faculty in this endeavor, we have leveraged the APIs of OpenAI tools to create Transcriptto, a Python program that contains clever algorithms that aid in the crucial steps in lecture preparation, allowing instructional designers and faculty to have a better starting point when starting the development of an online course. Transcriptto utilizes a straightforward yet robust workflow, incorporating openly available technologies such as Pymovie, FFmpeg, OpenAI’s Whisper, and ChatGPT. It transforms video lectures into polished text, supporting various input types, including audio files, and pre-existing scripts. The tool enhances clarity, removes redundancy, student interventions, semester-specific details, and provides a user-friendly configuration file for easy customization without requiring programming knowledge. The resulting content is optimized for information processing and fine-tuned via the OpenAI API, yielding not just a polished transcription that may be used to record a streamlined lecture more efficiently, but a multifaceted educational suite that generates outlines for slides, quizzes, assignments, and discussion questions. All learning objects are based on the content being taught.
Preliminary test usage of Transcriptto indicates faculty are enthusiastic about options for enhancing existing lecture materials and creating an optimized experience for their online course. We estimate this process will contribute to cutting the time it takes to write a script for studio sessions, and faculty will also appreciate the efficient creation of high-quality additional learning objects to evaluate students and maintain engagement. To assess the efficacy of Transcriptto, we employ a modified schema after Merriam [4], conducting a survey and focus groups with faculty users who provided their content as input for the program to process. Our aim is to gather in-depth insights, experiences, strengths, weaknesses, and suggestions related to the tool, actively facilitating discussions, and analyzing qualitative data to draw conclusions about its efficacy. The tool’s adaptability, driven by ongoing feedback from faculty, instructional designers, and other experts in the field, ensures its alignment with real-world educational needs.
Keywords: AI, Instructional Design, Online Transition, Online Education, e-learning, OpenAI, Python, Time Saving Strategies for Faculty, Lecture Capture, Repurposing Video Lectures.