2026 CSI Summer Conference: Teach Together

This presentation attempts to present multiple search tools, including AI-enhanced search, in historical and societal context. The session addresses both the democratizing potential and inherent limitations of each technological shift, focusing on aspects such as information accessibility, critical evaluation, and responsible use. Attendees will examine how biases have evolved rather than disappeared with each new technology, from editorial choices in print materials to algorithmic biases in search engines and AI tools. The workshop tackles known pitfalls of modern research tools, including SEO manipulation, commercial interests influencing search results, and AI "hallucinations" that present false information with unwarranted confidence. Participants will engage with strategies for developing critical information literacy skills tailored to each technological era, emphasizing the SIFT framework for evaluating accuracy, bias, and relevance in both traditional and AI-generated information. Through practical exercises, attendees will learn to balance the convenience of "insta-research" with thorough evaluation methods. The session aims to empower faculty and administration to guide students through the evolving information landscape while maintaining critical perspectives on both traditional and emerging research tools. The workshop begins with a half-hour description of the history of research tools, methodologies, and processes, beginning in the pre-digital age, continuing through the digital age, expanding in the Internet and Search Engine Era and culminating in the "AI Era," Benefits and drawback to each time period and its technologies are discussed. A brief review of critical thinking and information literacy is given. The next half hour involves discussing the nature, development, benefits, ethical issues, and information issues regarding AI tools and web-enhanced AI search. Generative AI tools, or "genAI," are built to approximate communicative language through statistical analysis and data reconfiguration. This method introduces a host of problems, including confabulations, hallucinations, artificial agency, and the tendency of users to rely on the first outputs of AI tools. An individual/group exercise follows in which participants use two handouts, one comparing the efficacy of AI tools and one with a suggested "search tool workflow" process, to decide which tools to use when researching certain topics, how those tools should be implemented in the search process, and how the user should be accountable/transparent regarding their research tool use. Finally, all participants come together for a discussion. The last segment is a search experience, in which participants utilize the various tools discussed in the presentation on their own, with feedback from the facilitator. This can be an individual or group experience, and the end result is that participants will be able to help their students and others use AI search tools, and other search tools, in an informed and responsible manner. Discussion questions will be encouraged throughout the session in order to facilitate presenter-facilitated, participant-directed, dialog. All attendees will receive handouts that discuss the benefits and drawbacks of various research technologies, common research principles when using AI, a comparison of the efficiency of various research tools, and suggestions for holistic research.

At a recent panel of a state’s most widespread and prominent employers, all three of the participants stated that they would hire no college graduates, or no new hires at all, unless the applicants had relevant and practical generative AI experience. When one of the attendees, an instructor, noted that “you cannot really do that, students will have ethical concerns and will not want to use AI,” the panelists indicated that did not matter, that there were ethical ways to use AI, and they would use them, or would not be employed by those companies.  In light of this conversation, and similar ones throughout the world, the instructional design, education, and workplace communities must facilitate a dialogue with each other and their students about WHEN and HOW to incorporate AI tools into teaching and learning experiences. This discussion must be contextualized with the expectations of the fields into which students will be entering when they leave instructional environments.  Participants will discuss tools and resources,trends in the workplace regarding AI use, and then have the rest of the time for and open discussion about questions and issues raised during the first parts of the presentation.  Artificial intelligence will eventually be used in many fields, even in ways that are currently inconceivable. This session will be a discussion on helping students develop human-centered patterns and perspectives NOW so they can rely on them when they move to the workplace.  AI pervades the world whether or not we want it. It is in a variety of standalone products and is incorporated into previous “AI-free” products. We must acknowledge this reality and incorporate these tools where they are reliably productive. Centering work, school, or lives, on the other hand, on a tool that is relatively transitory and constantly updating means relying on something with variable access, which could drastically degrade in quality, or, worse, be completely shut down.  In order to prepare for a workplace that has the most appropriate and usable tools, learners should practice with the same tools that they will use in the workplace. There will be little use learning on ChatGPT or Grok, for instance, if the student is going to use Claude or Perplexity during work. There is no use, furthermore, in learning exclusively how to use AI tools to search, OR research, when they do not have the capacity to learn how to control for the issues of these technologies. At the same time, there will be little use in learning an AI-free method of doing a task when employers will be looking for workers who know how to use AI.  Instructional designers and educators are responsible for instructing students in the best practices of their fields, how to do things using their own skills, and then how to use technologies such as AI if those technologies have been prominently and solidly integrated into their fields' common practices.  In an ideal, human-centered, AI-optional world, a learner would not use an AI tool to create something or produce something or carry out a task until they have MASTERED that skill. In academic terms, this would be three things, POSSIBLY four:  1. A master’s student  2. A college graduate  3. Someone with equivalent work experience   4. A student in their LAST SEMESTER of undergraduate experience   More pragmatically, students should only use genAI tools in their course to produce something or follow a process until they have DONE THE TASK BY THEMSELVES, until they have learned ENOUGH about the process or product to be able to understand the theory and reasoning behind its existence, its nature.   This connects to the concept put forth by Neil Postman in his book TECHNOPOLY that EVERY TEACHER should be a history teacher; students must learn how to do things manually, and the theories and debates and propositions about the relevant problems, processes, solutions, etc. in the field, before they use any technology, AI or otherwise, to do that task.