The technology used by educators and students has dramatically changed over the years, leading to advances that impact how certain subjects are taught and studied. Some researchers have even questioned whether these technologies are effective or even necessary in the classroom (Firmin et al., 2013). <\/p>\n\n\n\n
I have worked in Work Integrated Learning<\/strong> (WIL), aka Co-operative Education, for over 18 years. As a WIL practitioner, I closely follow the tenants described by Kolb and his ideas on how students learn effectively through experiential learning, repetition and reflection (1984). Within the last two years, the online facilitation of an undergraduate course that pertains to professional practice for engineers required my colleagues and me to try different methods of instruction for the first time. This included the adaptive learning<\/strong> strategy of implementing an AI\/machine learning software to review and critique student resumes. This has ultimately led to the creation of a research objective that covers the following:<\/p>\n\n\n\n Given my topic is based in the post-secondary education field, I would propose the best way to disseminate my plan would be to present the research paper in a peer-reviewed journal and present it to national and international organizations dedicated to “advocating for cooperative & work-integrated education programs within industry and educational institutions” (WACE, 2022). These professional organizations include provincial (ACEWIL<\/a>), federal (CEWIL<\/a>) and global (WACE<\/a>). The conferences hosted by these professional organizations in my industry require specific papers and projects to be peer-reviewed before being accepted to conferences. I will plan accordingly to have my research and findings completed before any formalized findings or comments on the research topic can be made.<\/p>\n\n\n\n When looking at whom would benefit from or use the research I proposed above, I consider other post-secondary WIL practitioners, such as guidance counsellors, career educators and practicum coaches. Those who create and write the machine learning code that would ultimately create the AI tools themselves could benefit from learning from this research, making them program better tools and products moving forward. If the research allows me to examine and explore the potential for certain biases (intentional and unintentional) in creating specific AI tools, it would be most beneficial to the public (and industry). To understand how to move away from this practice and determine a better method of creating adaptive technology that benefits all who use it.<\/p>\n\n\n\n References<\/strong><\/p>\n\n\n\n Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development<\/em> (Vol. 1). Englewood Cliffs, NJ: Prentice-Hall. https:\/\/www.researchgate.net\/publication\/235701029<\/a><\/p>\n\n\n\n