Background/Purpose: Depressive symptoms affect 280 million people worldwide, yet the quality of GenAI translations of depression screeners across languages is unclear. We developed a Translation Validity Index (TVI) to evaluate PHQ‑9 translations produced by ChatGPT, Copilot, and Google Translate in nine languages. Method: Two bilingual evaluators per language (N=18) rated each translation’s cultural appropriateness, grammar, and semantic clarity; TVI scores ≥3 indicated acceptable quality. Results: ChatGPT and Copilot generally met acceptability in high- and medium-resource languages (TVI=3.11–3.66), while Google Translate met acceptability for Ewe (TVI=3.73). Conclusions: TVI provides a structured approach for assessing forward and backward translation quality, but bilingual expert review remains essential when developing accurate mental health measures.
This session introduces an assignment where students are tasked with critically evaluating AI to reveal its limitations. To demonstrate the necessity for maintaining human intelligence over AI, students provide ChatGPT with a specific case study and prompt, then perform a forensic analysis of the output. Participants will see how this shift from "using" AI to "evaluating" AI moves the student from a passive consumer to a critical expert. By identifying hallucinations, inaccuracies, and lack of nuance in the AI’s response, students must rely on their own disciplinary knowledge to correct the record. This approach ensures human thinking remains the primary tool for validation, making the "human-in-the-loop" a visible and graded component of the learning process.
As artificial intelligence tools become embedded in students’ everyday learning practices, faculty must redesign courses to keep human judgment, disciplinary expertise, and ethical reasoning at the center of learning. This session presents a graduate course in Human Resources and Organizational Development focused on Digital Transformation and Artificial Intelligence in Organizations, which challenges students to critically evaluate AI-enabled practices across the employee lifecycle while designing responsible, human-centered organizational solutions. Rather than attempting to detect or prohibit AI use, the course employs authentic, discipline-specific assessments that require contextual analysis, organizational diagnosis, and ethical decision-making. These are tasks that cannot be completed meaningfully by AI alone. Participants will explore examples of assignments, project structures, and discussion strategies that prompt students to interrogate AI outputs, evaluate risk and bias, and apply professional expertise. The session raises broader questions about AI’s role in professional education while demonstrating how established pedagogical principles can guide responsible AI integration in graduate learning environments.
With all the enthusiasm and trepidation about the use of AI in academics, our value as educators might feel threatened. However, a key scene in the James Bond movie Skyfall can provide reassurance about the things we do better than AI, qualities that reflect our continuing value and demonstrate the deeper purpose that drives us.
Composition programs around generative AI often center on refusal, foregrounding instructors’ anxiety in front of AI as an emerging tool that is both disturbing and hard to harness. This session offers an alternative perspective by exploring how composition teachers might approach AI use with less certainty and more curiosity. In Spring 2026, I asked students in a first-year writing course to submit a form titled “Declaration of the Use of GenAI & Labor Statement” along with each major assignment. This form aligned classroom practice with UofL’s “Code of Student Rights and Responsibilities,” invited students to reflect ethically on their AI use, fostered mutual trust rather than surveillance, and made visible the student labor involved in their writing processes. In this session, I will share the form’s design and its rationale, and present an analysis of students’ disclosures.
Designing learning activities that develop students' self-awareness, critical thinking, and interpersonal skills can be particularly challenging in online asynchronous environments. Blackboard Ultra's AI tools, including Socratic reasoning and persona role play assignments, provide online instructors with avenues for developing students' critical thinking and interpersonal skills. In this demonstration, two AI assignments are shared that were developed for a coaching and talent development course. In the first AI assignment, students are asked to share their “facilitative coaching philosophy” with an AI persona who uses a Socratic questioning method to surface students’ underlying assumptions and motivations for coaching. The second AI assignment is a role play activity where each student interacts in the role of “facilitative coach” with an AI-persona “client” to help the client become aware of, and commit to self-generated SMART coaching goals. Learnings from student and instructor feedback regarding what worked well, and opportunities for assignment adjustments, are shared.
Business Communication choices have expanded in this new age of AI. It has never been easier to generate an email, report, or other professional document. However, human competency in business communication (Lucas & Rawlins, 2015) still requires being concise, clear, professional, evidence-driven and persuasive. AI messaging struggles to achieve these competencies in some ways and excels in others. This session will provide a working definition of AI communication competence in terms of authenticity, ownership, and relationship using on-going research from the Business Communication program in the Department of Management & Entrepreneurship. Additionally, we will suggest methods for implementation of these competencies in a classroom.
In a mid-level undergraduate history course, I asked students to reflect on what remains distinctly human about doing history alongside AI. Students encountered AI-generated interpretations of a piece of historical scholarship. They described the output as generic, overly confident, and less useful than reading, peer discussion, and sitting with uncertainty. Yet their own responses revealed a further tension: they did not always read AI-generated text to the same close, critical analysis they brought to assigned sources. My feedback sometimes mirrored their own critiques of AI, pointing to claims that were too general or insufficiently grounded in the text. Based on those responses, I redesigned a later assignment to ask not whether AI could interpret for students, but whether it could support narrower historical tasks without displacing human judgment. This session reflects on that trajectory in light of the American Historical Association’s emphasis on AI literacy and disciplinary judgment.
I redesigned my course’s final project of ethnographic assignment by shifting from a traditional academic paper to a digital storytelling format using the Adobe webpage. In the past semesters, I have noticed students were increasingly using AI generated narration in final projects, I chose not to restrict AI use but to refocus the assignment for deeper student engagement. The redesigned format encourages students to take creative ownership of their work through customized webpage layouts, integrated external links, blog elements, and original photos or videos. As a result, the final narrations have become more personalized and reflective of students’ authentic perspectives. I have noticed that the assignment redesign has improved the overall storytelling quality in the students’ submissions and strengthened their connection to the ethnographic process.
I am sharing an example of how students can use AI to engage with assigned course content, then demonstrate understanding and competency through a short video assignment. The assignment leverages AI as a support tool while preserving authentic voice, vulnerability, and connection. Students tend to be more genuine and reflective on video than in written assignments, often expressing insights that go beyond surface-level responses. It also allows faculty to see and hear students in a more personal way.
An assignment was created to help students move beyond simply understanding content to truly engaging with it on a personal level. The focus is on the history of racism in the US with particular attention to Black health and medicine, including the local context here in Louisville.
Students begin by working through a series of readings, recorded lectures, and videos that build foundational knowledge. As part of that process, they also complete two Implicit Association Tests, which often serve as a powerful entry point for self-reflection. For many students, this is the first time they’ve been asked to examine their own implicit biases in a structured way.
To demonstrate what they’ve learned, instead of a traditional paper or exam, students create a short 4–6 minute video. In that video, they respond to guided discussion prompts that ask them to connect the historical content with their own perspectives, reactions, and evolving understanding. The videos are then posted on a discussion board, where students are asked to view and respond to their peers' videos. This adds another layer of engagement, creating space for dialogue, shared perspectives, and deeper reflection. This balance is key.
Aș a Humanities instructor and a creative writer, I prize the messiness of human thinking. Since ChatGPT was first released in 2022, I’ve reclaimed my classroom and adapted most of my assignments so they now prioritize things LLM can’t do well, namely creativity and social interaction. I’ll offer some practical, easy to implement strategies for creating an unplugged classroom, everything from my “shoe organizer” cellphone policy to successfully assigning novels to a generation that many believe won’t read anything longer than a social media post. If you miss the sound of students talking, (hi!), please join me.
Students are conscious of the harmful effects AI has on learning, the environment, and society. Requiring them to use AI for an assignment, while well-intentioned, sends the message that AI is inevitable. By prioritizing critical thinking and student autonomy, giving students direct instruction on the variety of uses, types, and effects of AI and providing alternative assignments dispels this myth. Students should be encouraged to engage critically in how AI is shaping society, and instructors should cultivate a human-centered classroom culture of curiosity instead of compliance. While it’s increasingly harder to opt-out of AI, and not all AI is equally harmful, students who choose to resist AI should have opportunities to share and practice their views. Regardless of whether AI inevitability is true or not, this talk will pose important questions for students to consider when confronted with AI that encourage them to imagine a more equitable and sustainable future.
Generative AI tools are rapidly entering academic research and coursework, yet both students and faculty are still developing an understanding of how to use them responsibly and effectively. While many students have experimented with AI informally, they often lack experience applying it within an academic context. At the same time, many faculty are also navigating how AI fits into research practices, writing processes, and student assignments. This session introduces AI research appointments, a library-supported model that helps both students and faculty develop practical AI literacy through guided academic use. Modeled after traditional research consultations, these appointments provide individualized support for integrating AI tools into research workflows while maintaining ethical and scholarly standards.
NotebookLM is one of the tools that UofL faculty will gain with its new Google Gemini contract. This tool differs from other AI tools in important ways. For one, when writing answers to prompts, NotebookLM consults information that you import and control. You can import PDFs, text files, websites, or any relevant files. This process, known as Retrieval-Augmented Generation (RAG), increases the accuracy of its output, reducing the likelihood of "hallucinations" that are more common in AI chatbots. This presentation will introduce NotebookLM and show how it can be used for teaching updates and student support. We'll explore two focused use-cases: building searchable course notebooks that support students between office hours, and using web research features to efficiently audit and refresh course materials. No prior AI experience needed.
