Abstract: |
Students, parents, teachers, building and district administrators continuously are bombarded with educational technology software options that promise to engage students and raise achievement. The industry is growing, with the education technology industry projected to triple from $107 billion in 2015 to $350 billion by 2025 (Gilchrist, 2020). This proposed presentation focuses on an often-understudied aspect of educational technology – the intended and unintended impact of developers’ Discourses (being-doing-valuing combinations, (Gee, 2001) on the design, intent, and goals of an Intelligent Tutoring System (ITS). More specifically, the software these designers developed uses Artificial Intelligence (AI) to make individualized decisions and predictions based entirely on data collected from kindergarten through 2nd grade students. Hearing from designers about their intentions of the software helps identify how their personal and joined beliefs, values, and experiences in education impact the overall student experience. Snowball sampling (Goodman, 1961) was conducted with developers and programmers in a single educational software company. Semi-structured interviews were coded and interpreted through Discourse analysis (Gee, 2014). A list of 21 questions were used to guide the interview. Developers were encouraged to ask clarifying questions or take their responses in the direction they deemed appropriate. I would often ask follow-up questions on either defining software specific terminology, how developers viewed themselves as readers and memories reading from childhood, or their personal goals for the software. Interviews were recorded via Zoom and the live transcript feature was used to create a script of the conversation. The first round of coding will be to identify general themes including “values”, “past experiences”, “impact”, “pedagogy”, “social-emotional development”, “intentional outcome”, and “unintentional outcome”. The following coding phase will seek out Discourse within the interview including “student”, “educator”, “expert”, and others manifested. During the first two coding phases, individual interviews will have different themes and Discourse identified specific to the individual participant. On the next phase of coding the transcripts, the interviews will be compared and calibrated for commonalities in themes and Discourse.
Through this study, I first sought to understand how and why the particular software exists as it does. Hearing from the designers will help me know ways that the software is doing what it is intended to do and/or areas that I can offer suggestions to meet unanticipated, latent, and overt goals. This current research will ultimately impact my ability to design future targeted and impactful studies for the current ITS. Sharing the process and impact of starting with analyzing the Discourses of developers, prior to designing student focused studies, can influence the methods and research design of future studies in the field of AI and ITS. Explored further in this paper, is the opportunity for educational technology software developers to be agents of pedological and cultural change in education through the design, delivery, intended purpose, values, and impact of their software. |