Tag: Faculty

University of Arkansas Creates Faculty Learning Community

Effective teaching and learning are key elements of a student’s academic success, but ensuring professors have access to training, support and resources to employ best practices in the classroom can be a challenge for institutions.

At the University of Arkansas at Fayetteville’s Fulbright College of Arts and Sciences, Lynn Meade created the Faculty Learning Community to tackle this issue, uniting professors across disciplines to improve student learning and achievement.

The Faculty Learning Community, which launched this summer, strives to unite staff and faculty across campus to work toward the shared goal of student success, Meade said.

The background: Meade has worked as a communications professor at the University of Arkansas for two decades, but in 2023, she realized there were entire student support teams and departments that she didn’t know about. The university hosted an event on high-impact practices to invite stakeholders to share and learn from one another.

“I sat down at a table with a whole lot of student success people, and I was amazed at all the things that they were doing and how I could be on our campus for so long and [have] no idea the things that were going on behind the scenes, things students could take advantage of that they hadn’t yet,” Meade said.

This experience prompted her to get more involved with support staff and orchestrate opportunities for other professors to learn from one another across campus. “We need to find a way to integrate faculty and student success initiatives,” Meade said. “They need to know what one another are doing, they need to shake hands, make friends, have coffee, talk—the things that really make things happen, because our students, their success depends on us cooperating.”

Students are more likely to talk to a faculty member they trust than seek out a support office on campus, Meade said. “I think informing them not only how to teach their class well, but also how to integrate those resources, is really important.”

The result was the Fulbright Faculty Learning Community, a community of practice and faculty development program, which Meade now leads as director.

How it works: The program launched with four offerings for faculty: a course-building workshop, a reboot class to help with updating content, forums for sharing innovative teaching ideas and introductions to student success teams.

One of Meade’s goals is to avoid replicating existing efforts on campus but provide a one-stop shop to unify and amplify the great work taking place. “There’s so many cool resources on our campus, but there’s no one place they all exist,” she said.

The Fulbright Learning Community had its kickoff event this summer, engaging 14 faculty members in a three-hour workshop on course building.

The workshop invited faculty to consider students, rather than content, at the center of their syllabus, using a communications principle of audience and purpose. “I think if our audience is students and our purpose is to teach them, maybe we shouldn’t say, ‘I’m going to cover my material,’ but, ‘I’m going to think of ways that they can learn the material,’” Meade said.

Survey Says

A 2024 Student Voice survey by Inside Higher Ed and Generation Lab found that 40 percent of respondents believe their academic success would improve if professors connected in-classroom learning to issues outside the classroom or students’ career goals.

Some professors who are straight out of grad school may have only received teacher education or used material given to them by other faculty, Meade said. Others who have taught abroad but never in the U.S. may need some help adapting their materials for American students.

The learning community also invited career center professionals to showcase ways to embed career competencies in the syllabus and attach resources to their learning management system to help address career development for students. A future session will invite professors to share how they’re using and teaching generative AI tools.

“Faculty success equals student success,” Meade said. “The teachers are their first line [of support]; a lot of that success is what’s happening with the teacher. When we all work together on the same side, how we communicate with each other is going to make a big impact on the student retention.”

If your student success program has a unique feature or twist, we’d like to know about it. Click here to submit.

Source link

September 17, 2025
Supporting the Student Researcher: Effective Teaching, Learning, and Engagement Strategies – Faculty Focus

Higher education provides students with the opportunity to participate in a diverse range of academic programs that facilitate further exploration beyond the classroom. Participating in a guided research project offers valuable exposure to discipline-specific inquiry. Under the mentorship of a faculty research advisor, students gain critical skills such as project management, research methods, and early professional development. While the roles and responsibilities of the research advisor can vary depending on the type of research (e.g., human subjects, qualitative, quantitative, mixed methods), there are commonalities in how to best support student researchers. These strategies highlight best practices along with other important considerations for research advisors.

Traditionally, a research advisor guides and oversees student-led research by providing pivotal insight and direction throughout the project (National Academy of Sciences, 2009). The advisor plays a vital role in ensuring that the student follows ethical standards and guidelines. Beyond its formal role, research advisors also serve as mentors who motivate, encourage, and support students as they develop intellectually and professionally. They also create a protected and affirming space for students to be vulnerable in instances of uncertainty as the research project progresses. This dual function as both advisor and mentor has a lasting impact on students’ academic success and career trajectories. Through intentional mentorship and guidance, research advisors help students cultivate scholarly excellence, professional competence, and life skills that extend beyond the academic environment into daily activities.

There are several considerations that may cause faculty members to feel apprehensive about serving as research advisors, especially since these responsibilities extend beyond classroom instruction. This includes increased teaching loads, required committee responsibilities, professional development, community service, and administrative duties (Springer et al., 2023). Balancing these institutional demands can be overwhelming. These expanding roles and expectations contribute significantly to faculty fatigue, often leading to demoralization and persistent stress. Additionally, a student’s aptitude, skills, level of independence, and overall capacity for college-level research can also add to the complexity of serving as a research advisor.

Teaching, Learning, and Engagement Strategies

The advisor role aligns with several teaching and educational philosophies. Notably, Behaviorism (stimuli, reinforcement, and environment) and Essentialism (back to the basics) are all useful. These conceptual and philosophical frameworks are demonstrated in each of the posited strategies through the intentional use of engaging activities and techniques. Also instructive is Jean Piaget’s philosophy, which emphasizes a hands-on approach that focuses on active learning and interaction, which leads to the student’s self-exploration and discovery (Uzun, 2024). Promoting engagement that fosters problem-solving and critical thinking is especially important in the student-researcher-advisor relationship. Serving as a research advisor also requires employing an eclectic approach in relation to teaching and educational philosophies. Throughout each of the following strategies, it is imperative that the student’s learning and each engagement opportunity are meaningful, productive, and place the student researcher at the center of the collaboration. Each of these strategies presents teachable moments not only for students but also for the research advisor. The implementation of each plan to support the student, specifically how and why it is addressed.

Assess Students’ Readiness for Research

Conducting a low-stakes, informal assessment of the students’ interests, strengths, and areas of opportunity is a good starting point. Prior involvement with the student, due to their having taken a course with the faculty, may have already allowed for a preliminary assessment of the student’s preparedness for college-level research. In the absence of experience working with the student, one quick assessment method is to explore the student’s understanding of the research project more deeply. Engage in active listening, ask insightful and probing questions, and allow the process to unfold naturally. This encourages the student to demonstrate what they know about producing a noteworthy project. Students have a wide range of learning styles, competencies, and abilities. The goal is not to judge but to assess through an objective lens. This strategy enables faculty to make informed decisions about the commitment level, existing competencies, and overall student needs when undertaking scholarly research (Sharp, 2002).

Model Behavior and Coach for Success

Engaging students outside of the classroom also allows faculty to demonstrate other skill sets. The research advisor should conduct themselves in a manner that shows students ways to interact with various research stakeholders and how soft skills can be applied across different contexts. Much of the advisor’s time is spent monitoring and supporting the student. It is also imperative that the student is empowered and given the space to work independently, which is vital to students’ metacognition (Howlett, 2021). Research can be daunting; therefore, a key part of the coaching process involves implementing activities and providing affirming feedback that keeps the student engaged and interested. This strategy is essential because it supports students in gaining the requisite proficiencies as a researcher and collaborating with others.

Introduce and Teach Project Management Skills

Prioritizing tasks and effective time management are valuable skills that can enhance a student’s academic, professional, and personal life. Monitoring and evaluating overall goals and objectives promote the success of the research project. A practical approach to this strategy is to assign the student small tasks with specific timelines and deliverables, then provide incremental feedback. Scheduling project meetings to identify immediate tasks and priorities helps students understand and appreciate how to manage projects effectively. Clearly defined due dates, which may sometimes be inflexible, must be communicated (Sharp, 2002). Students are empowered when given relevant information, as well as a clear understanding of each phase of the research project. Adopting this strategy is a value-added benefit for students, as it reduces the likelihood of missed deadlines and critical deliverables throughout the project cycle.

Create a Safe Space for New Experiences and Growth

Naturally, some students may feel intimidated or unsure about how the student-faculty research collaboration will proceed. Uncertainty is expected since this is a new role and experience. It is notably different from previous instructor-student interactions that existed in the classroom. While the faculty advisor anticipates various outcomes, the student also has expectations related to faculty expertise and creativity, as well as benefiting from their full support and constructive criticism (Abbas, 2020). The research advisor must ensure that a safe environment is created for the student to feel empowered to share alternative or completely different viewpoints on various aspects of the research project. Maintaining a safe environment also encourages collaboration, student growth, and overall development. Using this strategy creates an environment of trust, teamwork, and mutual respect.

Finally, regardless of the size or type of institution, service and student engagement activities are viewed positively in higher education. Serving as a research advisor gives faculty the chance to showcase what they do outside the classroom. A large part of their time is dedicated to meeting teaching and learning responsibilities, for which they are evaluated on annually. Great effort is also made to ensure that faculty adequately support students with academic challenges. Taking on the role of research advisor also allows high-achieving students to be engaged. Additionally, this role supports and addresses the needs of students with aspirations for graduate studies, those seeking more challenging opportunities, or who have a general interest in scholarly research. Ultimately, it is a win-win situation for both the student and the faculty member who takes on this important role.

Curtis L. Todd, PhD, is Professor of Social Work in the School of Arts and Sciences at Atlanta Metropolitan State College, where he also provides instruction in Criminal Justice. Quintero J. Moore, MD, is Assistant Professor of Biology in the School of Arts and Sciences at Atlanta Metropolitan State College.

References

Abbas, A., Arrona-Palacios, A., Haruna H., and Alvarez-Sosa, D. 2020. Elements of students’ expectation towards teacher-student research collaboration in higher education. Frontiers in Education Conference (FIE): IEEE.

Howlett, M.A., McWilliams, M.A., Rademacher, K. et al. 2021. Investigating the Effects of Academic Coaching on College Students’ Metacognition. Innovative Higher Education: Springer Nature.

National Academy of Sciences, National Academy of Engineering (US) and Institute of Medicine (US) Committee on Science, Engineering, and Public Policy. (2009). On Being a Scientist: A Guide to Responsible Conduct in Research – Advising and Mentoring. 3rd Edition. National Academies Press: National Academy of Sciences.

Sharp, J.A., Peters, J., & Howard, K. (2002). The Management of a Student Research Project. 3rd Edition. Routledge: Taylor & Frances Group.

Springer, A., Oleksa-Marewska, K., Basinska-Zych, A., Werner, I., and Bialowas, S. (2023). Occupational burnout and chronic fatigue in the work of academic teachers-moderating role of selected health behaviours. National Library of Medicine: PLoS One.

Uzun, Aylin. 2024. Education Based on Piaget’s Theory. Research In Educational Sciences-I: IKSAD.

Source link

September 17, 2025
How We Think, How We Teach: Five Ways to Think About AI in Faculty Work – Faculty Focus

Every new technology brings with it a moment of reckoning and a lot of noise. Higher education has always had its share of both. We’re good at asking questions, kicking the tires, holding things at arm’s length until we’re sure it’s worth leaning in. But AI hasn’t given us that luxury. It arrived fast, and it arrived everywhere. And so here we are: adapting syllabi, revisiting assessments, trying to imagine what teaching looks like when the work of thinking and writing and making can now be shared with a machine.

It’s easy to feel like we’re supposed to become AI experts overnight, or like our value is being called into question. But I don’t think either is true. The real challenge, the real opportunity, is to understand how this new partner might show up in the work we already do. The intellectual work. The teaching work. The deeply human work.

That starts by asking better questions – not just “What can AI do?” but “What kind of thinking does good teaching really require?” If we can name that, we can start to see where AI fits and where it doesn’t.

Jared Spataro, Microsoft’s Corporate Vice President for AI at Work, offered a helpful frame for understanding AI’s potential. He identifies five key cognitive tasks that define knowledge work: perceiving, understanding, reasoning, executing, and creating. I think this framework can be translated to the world of higher ed, and the work that faculty do. Because whether we’re designing curriculum, guiding discussion, mentoring students, or shaping institutional strategy, we’re doing some blend of those five things. And by looking closely at how they show up in our work, we can start to imagine how AI might support, not replace, the best of what we do.

1. Perceiving

Perceiving is about seeing what’s really there – what’s in front of us, and what might be hidden underneath. It’s the first move of any good teacher or designer: noticing. Noticing what students understand and what they don’t. Noticing patterns in discussion boards, in assignment uploads, in the quiet absence of a student who was once engaged. Perception is where reflection begins.

AI can help here by extending human observation. Imagine tools that model thousands of student submissions and flag potential misunderstandings. Or dashboards that surface patterns in feedback across multiple course sections. Or sentiment analysis that gives faculty a pulse on how students are responding to a unit in real time. These aren’t just speculative. Georgia State University’s implementation of predictive analytics has significantly improved student outcomes, especially for underrepresented groups (Dimeo, 2017).

And in my own work, I’ve seen how AI-powered tagging and clustering can help make sense of the digital exhaust students leave behind. During a review of some end of course survey responses, I used a language model to surface common themes in open-ended student responses. What might have taken hours of coding was compressed to minutes, giving me more time to focus on what really matters: how to respond, how to improve, how to connect.

When we talk about perceiving, we’re really talking about attention. AI can expand the reach of our attention—but it’s still up to us to decide where to look, and what to do with what we find.

2. Understanding

Understanding sits at the core of what faculty do. Whether we’re preparing to teach a new course or guiding a student through their first research project or writing up our own research, we’re spending time interpreting. This kind of work takes time and attention, a willingness to sit with uncertainty. And it’s where AI, when used carefully and with intention, can help.

In my own experience, I’ve used generative tools to scan large sets of institutional policy documents to better understand how decisions are communicated, and where inconsistencies emerge. What would have taken a full afternoon of toggling between tabs and highlighting paragraphs became a manageable, interpretable task, one that still needed my judgment, but got me there faster.

When AI can support us in making sense of large volumes of information, summarizing texts, comparing perspectives, identifying patterns, we free ourselves up for the more valuable intellectual work: asking better questions, and spending more time with the answers that matter. It’s about capacity.

Recent studies point to this as a growing area of impact. In a 2023 EDUCAUSE report, researchers note that AI’s ability to “curate and synthesize complex information” has emerged as a top priority for institutions looking to support both faculty productivity and student success (Pelletier et al., 2023). That doesn’t mean outsourcing the work of understanding, but it might mean sharing the load.

As Spataro puts it, these systems can “interpret, analyze, and generate vast amounts of text data,” but it’s up to us to bring interpretation, context, and care (2025). Used well, they don’t replace the act of understanding but they expand the space we have to do it well.

3. Reasoning

Tools that can break a complex task into parts, hold multiple threads in play, and adjust course as they go? That’s thinking and reasoning, and it can be put to work for us. Imagine planning a new course. You’re juggling student needs, institutional requirements, disciplinary content, pedagogical practices, assessment design, and accessibility considerations. AI can now meaningfully assist in that process by helping you reason your way through the options. I’ve used it to test weekly structures, re-sequence modules, generate alternate assessments keyed to different learning outcomes. It’s not always right. But it’s responsive.

This is what Spataro points to when he describes reasoning models’ capacity to navigate multistep challenges. And it’s what others are beginning to explore too. The 2024 Stanford Institute for Human-Centered AI report notes that models like GPT-4 are now outperforming the average human on tasks like LSAT logical reasoning questions, tasks that require inference, not recall (Stanford HAI, 2024). It’s not just that AI can make suggestions. It can anticipate consequences. It can debug your logic. It can help you think.

But it doesn’t replace the thinking. That means we need to stay in the loop. Because just like a teaching assistant who works fast but occasionally misses the nuance, these tools need supervision. The real value is in the collaboration. You bring the goals, the context, the judgment. The system brings the speed, the range, and the willingness to try again.

4. Executing

One of the most immediate shifts many faculty feel with these tools is in execution. Not in some futuristic, sci-fi sense, but in the simplest, most grounded way: things just get done faster. And not just routine things. Writing the first draft of an announcement. Reformatting a rubric. Creating a visual from a block of text. Summarizing student feedback across discussion boards. These are tasks that used to chip away at your time, that required a certain kind of attention and structure you didn’t always have at the end of the day. Now they can happen in seconds. Not perfect, but done. Or at least started, ready for you to refine, revise, and finalize.

In my own work, this means I don’t get stalled as easily. If a meeting runs long and I lose the hour I had planned to draft a guidance doc for a new course design initiative, I don’t start from zero later. I sketch the intent in a few lines, and the system scaffolds a first version. I get to come in as editor, refining and recentering. And yes, sometimes rejecting and starting over.

What’s changed isn’t just speed. It’s how close we can get from idea to action without needing to switch tools, start a new doc, find the right template. Execution becomes lighter. It gets folded into the flow. And for faculty navigating a day that might include grading, advising, committee work, and prepping for class, that lightness matters. But we should be careful here. The goal isn’t to turn every task into a race to the bottom. The speed is a gift only if we use the time it gives us well. Execution, in this new context, isn’t about doing more. It’s about clearing space to do what matters.

5. Creating

Creativity sits at the heart of so much work that faculty do. It’s how we see ourselves, not just as transmitters of knowledge, but as makers. We write, design, shape experiences. We revise courses to better fit the needs of a new cohort, craft discussion prompts that pull students deeper, build assignments that didn’t exist five years ago. Creativity is where our identities as scholars, teachers, and thinkers converge. So it’s no surprise that when people hear about AI “creating,” it sparks something between skepticism and alarm. And I get that.

But here’s where I land: this kind of creativity isn’t competition. These tools don’t originate like we do. They don’t generate ideas out of passion or lived experience. But they can be astonishingly good at offering sparks, those half-formed ideas, raw drafts, unexpected juxtapositions. In my own work, I’ve used them to draft module overviews that I later rewrite completely, but which help me see where I’m being too vague or too dense. I’ve used them to riff on potential assignment prompts, not to choose one blindly, but to scan for a new angle or a better tone. Sometimes, I reject it all. But I always walk away with more clarity about what I think.

That’s the shift: using the tools not to replace our voice, but to sharpen it. Not to outsource our thinking, but to reflect it back in new forms. Of course, this only works if we stay present in the process. If we hold fast to our criticality, our nuance, our sense of context. That’s the work. That’s the art. And as Spataro reminds us, the best ideas don’t care where they came from—they care what we do with them next (2024).

Why This Matters

I was working with a group of faculty from different disciplines demoing a few uses of generative tools in course development. We’d just finished a quick example, generating some low-stakes writing prompts for a discussion board. One person leaned back, arms crossed. “This is fine,” they said. “But the question is: what kind of teacher does this make me?”

It’s a great question. And I think the answer is: it makes you a teacher who’s choosing. Choosing how to spend your time and choosing where your expertise matters most. Choosing when to hand something off to the machine and, most importantly, choosing when to hold on tight because the human parts are the whole point.

That’s why I’ve stayed close to these five cognitive tasks. Because none of this matters unless we connect it to the real work we do. The knowledge work. The pedagogical labor. The thinking, the care, the creative decisions.

These five domains – perceiving, understanding, reasoning, executing, creating – aren’t abstract categories or corporate taxonomies. They’re a mirror of our everyday academic labor. They map how faculty prep a new course, evaluate student performance, write feedback, collaborate with colleagues, design new programs, interpret policy, serve on committees, apply for grants, rethink curriculum. This is what it means to work in higher ed. And these are the places where AI is entering.

So when we talk about adoption or training or integration, we’re not just talking about tools or workflows. We’re talking about how we think. How we value time. How we make meaning. And whether we can build systems, technological and human, that let us spend more of our energy on the parts of this job that matter most.

Dr. Nathan Pritts is Professor and Program Chair for First Year Writing at the University of Arizona Global Campus where he also serves as University Faculty Fellow for AI Strategy. He leads initiatives at the intersection of pedagogy, design, and emerging technologies and has spearheaded efforts in the strategic implementation of online learning tools, faculty training, and scalable interventions that support both educators and students. His work brings a humanistic lens to the integration of AI—balancing innovation with thoughtful pedagogy and student-centered design. As author and researcher, Dr. Pritts has published widely on topics including digital pedagogy, AI-enhanced curriculum design, assessment strategies, and the future of higher education.

References

Dimeo, J. (2017, July 18). Georgia State improves student outcomes with data. Inside Higher Ed. https://www.insidehighered.com/digital-learning/article/2017/07/19/georgia-state-improves-student-outcomes-data

Pelletier, K., Robert, J., Muscanell, N., McCormack, M., Reeves, J., Arbino, N., & Grajek, S., with Birdwell, T., Liu, D., Mandernach, J., Moore, A., Porcaro, A., Rutledge, R., & Zimmern, J. (2023). 2023 EDUCAUSE Horizon Report: Teaching and Learning Edition. EDUCAUSE.

Pritts, N. (2025, March 19). Supporting the Instructional Design Process: Stress-Testing Assignments with AI. Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/supporting-the-instructional-design-process-stress-testing-assignments-with-ai/

Spataro, J. (2025). AI at Work: Coming to Terms with AI’s Cognitive Inflection Point. Microsoft WorkLab. https://www.microsoft.com/en-us/worklab/ai-at-work-coming-to-terms-with-ais-cognitive-inflection-point

Stanford Institute for Human-Centered AI. (2024). AI Index Report 2024. https://aiindex.stanford.edu/report/

Source link

September 15, 2025
3 More Faculty, Staff Removed for Kirk Comments

Right-wing X accounts are pushing for universities to dismiss any employees that have disparaged Kirk in the days following his death.

Photo illustration by Inside Higher Ed | LeoPatrizi/E+/Getty Images

At least five faculty and staff members have been fired so far for comments they made in response to the death of Turning Point USA founder and conservative firebrand Charlie Kirk, who was shot and killed Wednesday during an event at Utah Valley University.

Investigators announced Friday they arrested a suspect, 22-year-old Tyler Robinson, who is now being held in a Utah jail without bail. Utah governor Spencer Cox said during a press conference Friday that a family friend turned Robinson in to authorities after the suspect suggested to a relative that he’d killed Kirk. Robinson was not a student at Utah Valley.

The Utah Board of Higher Education said in a statement that Robinson is a third-year student in the electrical apprenticeship program at Dixie Technical College and that he attended Utah State University for one semester in 2021.

Among the latest college employees terminated for their responses to Kirk’s killing, Lisa Greenlee was removed as a part-time instructor from Guilford Technical Community College in Jamestown, N.C., on Thursday after she made comments criticizing Kirk to students during an online class, saying, “I’ll praise the shooter; he had good aim.” A video of her remarks made the rounds on X, where right-wing accounts encouraged the college to fire her.

“We deeply regret that students, employees, and the community were impacted by her comments. Greenlee’s behavior is not consistent with the college’s values and mission to serve Guilford County. Her statement regarding the assassination of Charlie Kirk does not support the open and respectful learning and working environment that GTCC provides every day,” GTCC president Anthony Clarke said in a statement. “We want to reiterate that supporting violence is reprehensible and will not be tolerated at the college.” Greenlee did not respond to Inside Higher Ed’s request for comment.

Two employees at Cumberland University in Lebanon, Tenn., were dismissed Thursday for making “inappropriate comments on the internet related to the tragic shooting of Charlie Kirk,” university president Paul Stumb wrote in a statement posted on X. He identified the employees as Michael Rex, an English and creative writing professor, and Max Woods, an assistant esports coach, but he did not share what they said. Like Greenlee, both had been the subject of online campaigns advocating for their firing.

“This decision was not made lightly,” Stumb wrote. “We understand the importance and the impact of this action, and we want to emphasize that we conducted a comprehensive investigation prior to making our decision.”

Before Stumb’s statement was publicized, Rex posted an apology on his Facebook page. “No one deserves to be murdered,” he wrote. “I did not think about the pain and anger that my words would create. My comments were not meant to celebrate nor to foster political violence and for any traums [sic] my words caused, I am truly sorry.” Rex and Wood did not respond to Inside Higher Ed’s request for comment.

The recent firings follow the dismissals of Laura Sosh-Lightsy, a student affairs administrator at Middle Tennessee State University, and an unnamed staff member at the University of Mississippi.

A Clemson university professor is also subject to an ongoing push by X users to have him fired for statements on Kirk’s death. On Friday afternoon, the university posted a statement that alluded to the situation. “We stand firmly on the principles of the U.S. Constitution, including the protection of free speech. However, that right does not extend to speech that incites harm or undermines the dignity of others. We will take appropriate action for speech that constitutes a genuine threat which is not protected by the Constitution.”

Source link

September 12, 2025
The Urgency for Outreach to Shy College Students – Faculty Focus

Shy students do not come to office hours nor seek the advice of faculty members. In the college classroom, they are quiet and reserved and may appear nervous or lacking confidence. Professors know the keys to success in college and gladly share these with students who ask our advice or visit our office, but assertive outreach is the only way to share critical advice with the shy student. What they do not know can harm them or at least set them back relative to peers, especially worrisome if they are first generation, lower income, or underrepresented minority students. Many outreached hands are needed to support and advise these students who lack the assertiveness to come to us. We must go to them.

In this article, I will review why many students appear shy in the college classroom and the keys to success every college student needs to hear from us in their freshman and sophomore years. Such outreach supports student retention, providing practical advice to help students navigate the college landscape toward successful completion of their degree.

Empathize with Shy Students in the Classroom

Better mentoring begins with empathy, so reflect upon a time when the college classroom was an intimidating place. Shy students are thinking much the same as we did in our first year of college. They can feel insecure and self-conscious, making social comparisons in an environment where grading makes explicit the varying levels of ability. They are inexperienced young adults, unsure of themselves or their competence (Medialle & Usinger, 2019). Consider the psychology of shy students within the unique performance environment of the university.

Class Participation

Shy students are afraid to make mistakes, fearing negative social judgment. Faculty contribute to this anxiety when we grade for class participation in our syllabus, so it is important to balance this practice with gentle encouragement to speak up and engage. Students adopt a risk-avoidance approach to classroom participation and so calculate potential benefits (reward for my effort), odds of success, and risk of not performing well, resulting in their level of effort calculation (B-O-R-E; Hendricson, 2014). Instructors can encourage class participation with verbal praise for positive contributions and affirming words for mistakes. Students’ perceived odds of success and risk can be shaped by our question (e.g., request a recap rather than extrapolation), by our thoughtful lead-up to the question (e.g., Q&A to draw a conclusion), or by offering help when met with silence (“the correct answer starts with a C … anybody?”). Be sensitive to the shy student’s thinking while in the Q&A environment and allow their initial silence to be a moment of calculation before action.

Power Dynamics in the Classroom

Adding to the intimidating nature of the classroom interaction, instructor power is both manifest and latent. We control course grades, write letters of recommendation, and grant access to professional networks. But we can also use latent authority to set a positive classroom tone: Be welcoming to all, learn students’ names and use them, and be happy during class or seem to be (act happy). Avoid teasing, sarcasm, and mockery as forms of humor as these may be poorly received. To shy students, an instructor’s outreached hand reminds them that you are a supportive approachable figure rather than a formidable authority. Be sensitive to the shy student’s perception of the classroom power dynamic and be careful how you wield this power.

Not Knowing the Unwritten Rules of College

Shy students more slowly engage and learn the common behaviors, values, and beliefs gotten only through their interactions with people on campus. Mintz (2024) reviewed college’s “hidden curriculum”, the institutional culture, expected behavior, and unspoken norms that influence academic success. Asking questions in class and help-seeking behavior for writing or tutoring services, although normal for college students, could be a first time experience for some shy students. They may associate stigma or shame with needing help, thinking “I’ll figure it out on my own” or “I should be able to do this”. Chatting informally with professors before or after class can seem awkward, especially if shy students view the professor as too busy, too important, or too different from me to be bothered. Visiting office hours is also common for college students and is an effective icebreaker to view faculty as supportive approachable figures, but it depends fully upon students asserting themselves. While just as important as the academic curriculum, the lessons of college’s hidden curriculum can take longer to reach students who are members of underrepresented groups (Smith, 2015). Not knowing the common rules of conduct can lead to missed opportunities that later become regrets.

Being Different, Blazing New Trails

As implied in several observations above, the new university environment may hold achievement-oriented beliefs, practices, and values that differ dramatically from a new college student’s home and family culture (Pyne & Means, 2013). Self-advocacy can feel selfish, and help-seeking can feel like evidence that college success is beyond one’s current competencies. For the first-generation trailblazers, attending college alone is a major step when they are deviating from family traditions, practices, or expectations (Cerezo, Lyda, Beristianos, Enriquez & Connor, 2013; Martin, Galentino & Townsend, 2014). Be sensitive to the shy student’s imposter syndrome, that intimate feeling that you may not belong here common in first-year students who lack a role model to help them navigate their college years (e.g., Nelson, 2024).

Early Advice that All College Students Need to Hear

Mentors with college experience can empathize with missed opportunities that became regrets. The advice many of us give today likely consists of some of those “what I wish I had known then” lessons. Because shy students do not come to office hours nor seek our advice, there is an urgency for outreach and mentoring. Shy students do not know what they do not know, and what they don’t know now can harm them in the near future. Help shy students, and all students, avoid these four pitfalls.

Protect Your Grade Point Average (GPA)

Students need a high GPA above 3.0 to compete for the better jobs or when applying for post-baccalaureate studies. GPA is a simple numerical indicator of overall abilities compared to other applicants, and it is no surprise that busy employers prefer the demonstrated work ethic of ‘A’ students over that of ‘C’ students. Once broken, a student’s GPA cannot be fixed without retaking courses for a higher grade, a time-consuming and limited option. Grades for courses in the major should be highest, ideally straight As, because this is supposed to be your favorite topic. A GPA below 3.0 weakens your entire application package for post-baccalaureate studies. Those admissions committees also like to see a steady increase in GPA into your junior and senior years, showing improved learning skills. We have seen too many students wait until senior year to decide to pursue graduate or professional school only to discover that a grade point average below 3.0 limits their opportunities.

Get Noticed in the Classroom. Instructors Write References.

Students influence the future content of job references and letters of recommendation by their own actions and words in the presence of faculty letter writers. Too few students are advised to get noticed early in the classroom by their professors, even though most students will need references or recommendations for their first job or for applications to post-baccalaureate study. Faculty write strong letters for students we remember, the ones who stood out in class because they showed enthusiasm and inquisitiveness (Gomez, 2016). Weak letters of recommendation come from professors with little memory of a students’ classroom performance and with no long-term student-faculty relationship. Advise students to participate in class to highlight the academic prowess, personality, and social skills they want future letter writers to remember. Advise students to take only in-person courses with potential future letter writers, because online courses with minimal personal interaction leave a weaker impression on the instructor’s memory. If students sit passively and quietly in class, do not get involved in the major, and do not interact with faculty in memorable ways, it is difficult for faculty to write strong letters of recommendation when called upon. This will be the case even for students who are highly intelligent with good grades, strong test scores, and immense potential.

Refine Essential Skills

Students may not realize the importance of refining essential next-level skills, such as writing scholarly prose, developing spoken communication skills, and mastering the efficient use of library resources. Students need to know that every writing assignment is a chance to improve through thoughtful feedback from an experienced content expert reading your paper. Every oral presentation is your only chance to improve public speaking skills needed in every future career. Every group project is a chance to practice teamwork because future job interviews will ask about experiences where you collaborated effectively with others to achieve a shared goal (Smith, 2023). All students must master searching library resources and consolidating scattered facts into a coherent argument or essay. After the completion of first-year English composition courses, it can be easy for faculty within the major to ignore the importance of essential competencies, but weak communication skills show up later in job applications, in-person interviews, and writing samples.

Find a Mentor to Guide You

Finally, shy students need to know that mentors teach students about college opportunities and how to avoid mistakes. There is no better way to learn about the vast opportunities in a specific professional field than in a one-on-one mentoring relationship with a seasoned professional. Mentors help students find the right degree and ensure that college choices contribute to developing essential career-relevant skills that lead to a thriving career (Pascarella & Terenzini, 2005; Kuh & Hu, 2001). Outreach is especially important for ethnic minority, lower income, and first-generation college students for whom attrition rates are especially worrisome (Keels, 2013; Museus, 2011). For first-generation students in particular, mentors offer the same advice that second-generation college students allegedly benefit from, i.e., the advice college educated parents give to their own college-aged children. Reach out to shy students to help them plan a road map to career success and learn the hidden opportunities available during the college years.

Active Outreach Facilitates Student Development and Supports Retention

Key contributors to college student success include frequency of contact with professors, mentor support, and high expectations (Baker, 2013; Huss, Randall, Patry, Davis & Hansen, 2002; Yorke & Thomas, 2003). Interactions with faculty members are more significant in the early years of college (Schreiner, 2010). It is mentors — faculty, college staff, and even older peers — who shape the student’s developmental process through active relationships that psychologists suggest transform personal identity from high school student to career-ready college graduate (Chickering & Reisser, 1993; Erikson, 1968). College mentors should focus upon career planning in the freshman and sophomore years to facilitate that identity shift needed for undergraduates to envision their future professional selves (Martin, Spolander, Imran & Beulah, 2014; Perez, Cromley & Kaplan, 2013). Interactions with faculty also play a positive role in student persistence (Pascarella & Terenzini, 2005), specifically being supportive of student needs, being approachable, and returning phone calls and emails in a timely fashion (Lundquist, Spalding & Landrum, 2002-2003). Faculty mentors facilitate student development and support retention by encouraging college students to start thinking strategically about their career options and possible futures.

Shy college students may lack the assertiveness to come to us, so we must go to them. Help shy students avoid pitfalls during their college years and teach them the importance of high grades, mentoring relationships, and essential communication skills. More outreached hands in the freshman and sophomore years remind students that faculty are supportive approachable figures rather than formidable authorities. Shy students are inexperienced young adults and need our support to reach their fullest potential.

John Gomez is a professor of psychology in his twenty fifth year of teaching and mentoring undergraduate students at Our Lady of the Lake University, a Hispanic-serving institution located in San Antonio, Texas. His research program and teaching focus upon helping undergraduate students to think strategically about their future professional selves, with special attention to mentees’ development of identity and purpose in life. A research focus has been on ethnic minority, lower income, and first-generation college students, for whom national college retention rates are most troubling. He mentors college students about academic self-preparation, graduate school decision-making, and career planning in psychology and related fields. He also mentors first-time college instructors about classroom management and career readiness. He has delivered peer-reviewed research talks at multiple mentoring conferences, including the Mentoring Institute (University of New Mexico) and the International Mentoring Association (University of Florida).

References

Baker, C. (2013) Social support and success in higher education: The influence of on-campus support. Urban Review, 45(5), 632-650.

Cerezo, A., Lyda, J., Beristianos, M., Enriquez, A. & Connor, M. (2013) Latino men in college: Giving voice to their struggles and triumphs. Psychology of Men and Masculinity, 14(4), 352-362.

Chickering A. & Reisser, L. (1993). Education and identity, 2nd ed. San Francisco: Jossey-Bass.

Erikson, E. (1968). Identity: Youth and crisis. New York, NY: W. W. Norton & Co.

Gomez, J. (2016 Spring). How to properly request letters of recommendation from your professors: Ask, don’t tell. Eye on Psi Chi magazine. American Psychological Association.

Hendricson, W. (2014) Interpreting a Student’s Apparent Lack of Motivation. Academy for Academic Leadership News. 2013; 3(1). DOI: 10.13140/RG.2.1.5097.2644/1

Huss, M., Randall, B., Patry, M., Davis, S. & Hansen, D. (2002). Factors influencing self-rated preparedness for graduate school: A survey of graduate students. Teaching of Psychology, 29(4), 275- 281.

Keels, M. (2013) Getting them enrolled is only half the battle: College success as a function of race or ethnicity, gender, and class, American Journal of Orthopsychiatry, 83(2,3) 310-322.

Kuh, G. & Hu, S. (2001). The effects of student-faculty interactions in the 1990s. Review of Higher Education, 2, 309-332.

Lundquist, C., Spalding, R. & Landrum, E. (2002-2003). College students’ thoughts about leaving the university: The impact of faculty attitudes and behaviors. Journal of College Student Retention, 4, 123- 133.

Martin, K., Galentino, R. & Townsend, L. (2014) Community College Student Success: The role of Motivation and Self-Empowerment, Community College Review, 42(3), 221-241.

Martin, L., Spolander, G., Imran, A. & Beulah, M. (2014) Evolution of student identity: A case of caveat emptor, Journal of Further & Higher Education, 38(2), 200-210.

Medialle, A. & Usinger, J. (2019 March) Engaging quiet students in the college classroom. College Teaching, 67(2):130-137. DOI:10.1080/87567555.2019.1579701.

Mintz, S. (2024 May 16) Life Prep 101. Inside Higher Education online magazine. https://www.insidehighered.com/opinion/blogs/higher-ed-gamma/2024/05/16/how-hidden-curriculum-prepares-students-post-college-life

Museus, S. (2011) Generating ethnic minority student success (GEMS): A qualitative analysis of high-performing institutions, Journal of Diversity in Higher Education, 4(3), 147-162.

Nelson, R. (2024 Winter) Succeeding in college without falling into the imposter phenomenon trap. Eye on Psi Chi magazine. Washington, DC: American Psychological Association.

Pascarella, E. & Terenzini, P. (2005). How college affects students, Vol. 2, A third decade of research. San Francisco, CA: Jossey-Bass.

Pyne, K. & Means, D. (2013) Underrepresented and In/visible: A Hispanic first-generation student’s narratives of college, Journal of Diversity in Higher Education, 6(3), 186-19.

Perez, T., Cromley, J. & Kaplan, A. (2013) The role of identity development, values, and costs in college STEM retention, Journal of Educational Psychology, doi: 10.1037/a0034027.

Schreiner, L. (2010) The critical role of faculty and faculty development in sophomore success. In Hunter, M. S., Tobolowsky, B. F. & Gardner, J. N., (Eds.) Helping sophomores succeed: Understanding and improving the second-year experience. San Francisco, CA: Jossey-Bass.

Smith, A. (2023 April 25) Teamwork is hard work. Here’s how to build the skills to do it well. Forbes online magazine. https://www.forbes.com/sites/annkowalsmith/2023/04/25/teamwork-is-hard-work-heres-how-to-build-the-skills-to-do-it-well/

Smith, B. (2015 March 24) Mentoring at-risk students through the hidden curriculum of higher education. Lexington Books.

Yorke, M. & Thomas, L. (2003). Improving the retention of students from lower socio-economic groups, Journal of Higher Education Policy and Management, 25(1), 63-74.

Source link

September 12, 2025
How To Teach With AI Transparency Statements – Faculty Focus

I remember talking to my students about GenAI for the first time, almost three years ago. We covered the uses and limitations of some tools. We talked about the danger of losing our own voice. We talked about the importance of writing in our own words, even if the end product isn’t as neat as AI-generated prose. I remember taking over the conversation, asserting what GenAI is good for and what it’s bad at. I fell into a pattern of announcing “this is fine” and “don’t do that.”

At the time, it was the best I could do.

Since then, I have spent a lot of time refining my approach to GenAI in the classroom. I realized that my initial approach – though sound at the time – deprived my students of agency. I allowed my students to share their ideas about the technology. But then, I stepped in to make all the decisions that really mattered.

My approach now is very different. I ask my students to design their own writing processes, implement them, and reflect on them. Some of my students work GenAI into the process, while others prefer to leave it alone. I empower my students to decide whether and how GenAI should play a role in their work. At the same time, I want them to be mindful of how easy it is to give our voices away when we use this technology.

The key to making this all work is AI Transparency Statements.

We can use these statements to encourage our students to use GenAI responsibly and to think more deeply about their relationships to this technology, while also building a culture of trust and accountability. They are invaluable.

In the rest of this article, I will cover the two actions I ask students to complete in their AI Transparency Statements. Feel free to replicate and adapt them.

Step 1: Students describe how they used GenAI, according to our AI scale

As mentioned above, my students design multi-step processes for completing their projects. In the first part of the AI Transparency Statement, I ask my students to go back to those steps and self-report if and how they used AI. And depending on the context, I sometimes ask them to submit chat transcripts with their statement.

I refer them to the AI Assessment Scale (AIAS), which was developed by Leon Furze, Mike Perkins, Jasper Roe, and Jason MacVaugh. This scale assigns numbers for levels of GenAI usage, ranging from 1 to 5. A 1 means the student used no GenAI. A 2 means that the student used GenAI for pre-task activities like planning and brainstorming. A 3 means that the student used GenAI for assistance in completing the task itself. For example, some students use GenAI programs to get feedback. Or they use it for rephrasing some sentences or a paragraph. A 4 means that the student used GenAI to complete the task, with their direction. A 5 means that the student is using GenAI to find innovative solutions to a problem and to boost their own creativity.

Often, professors treat the AIAS as a faculty-facing tool. For example, we might tell students to stick to level 2 for an essay outline: they can use a GenAI program for brainstorming and getting started, but we want them to create the outline themselves.

But for me, the real power of the AIAS is as a student-facing tool. We can ask students to give the different parts of their process a rating, and then walk through why they gave them that rating. This encourages responsible AI usage, as well as meta-awareness.

Step 2: Students defend their use or non-use of GenAI

Once students assign AIAS numbers to their process, I then ask them to defend their use or non-use of GenAI. Why did they feel that level-3 usage of GenAI was appropriate for that part in the process? What would have been lost if they went down to a level-2 usage or up to a level-4 usage?

As they defend their use or non-use of GenAI, I want them to think about power and personal voice. Did that specific use of GenAI empower them or did it take their power away?

The goal, here, is to encourage students to link their decisions to larger questions about personal empowerment. After all, the responsible use of AI isn’t just about upholding academic integrity. It’s about making decisions that don’t give away our voice.

Final Thoughts

Using AI Transparency Statements doesn’t AI-proof assignments, if such a thing were even possible. Rather, these are a chance to highlight the importance of self-reflection and intentionality as we interact with these systems — not only in classes, but in our everyday lives.

Without these skills, it’s incredibly easy to lose ourselves.

Jason Gulya is a Professor of English and Communications at Berkeley College, where he chairs the AI and Academic Integrity Committee and serves on the AI Task Force. His first book was titled “Allegory in Enlightenment Britain: Literary Abominations” (Palgrave Macmillan, 2022). He also co-wrote a book titled “Artificial Intelligence, Real Literacy.” (2025) with Paul Matthews. He’s currently researching the rise of GenAI, process-based teaching, and alternative assessment.

Reference

Furze, Leon. “Updating the AI Assessment Scale,” https://leonfurze.com/2024/08/28/updating-the-ai-assessment-scale/

Source link

September 10, 2025

More Than a Name: How Assignment Labels Influence Student Learning and Performance – Faculty Focus

In higher education, assignment titles often serve a functional purpose, indicating the sequence of assignments rather than their specific objectives. However, intentionally structuring assignment titles and formats can significantly influence student engagement and learning outcomes, especially in online courses. Applying adult learning theory, we redesigned assignments to emphasize process-based learning and offer scaffolded opportunities for students to engage with authentic tasks. This approach clarified expectations, enhanced performance, and fostered a more collaborative relationship between faculty and instructional designers.

We grounded our redesign in adult learning theory (andragogy) because our student population skews older, with an average age of 25, and many are returning learners balancing coursework with careers and family responsibilities. Adult learning theory emphasizes that adults learn best when they understand why they are learning something, see clear relevance to real-world contexts, and are given opportunities to reflect and apply their knowledge authentically. With these principles in mind, we aligned assignments with professional scenarios, built feedback opportunities into the workflow, and broke tasks into logical phases to support reflection and critical thinking.

Building on this foundation, we reconsidered how assignments were labeled and structured. Traditionally, assignments were labeled “Homework 1” or “Homework 2.” These labels communicated sequence but not purpose. To foster student understanding and engagement in a Prescriptive Analytics course that focuses on model building and model analysis, we restructured assignments around two key phases—Model Setup and Model Insights—and renamed them using a consistent structure—Model Setup and Model Insights—followed by a number (e.g., Model Setup 3, Model Insights 3). This format clarified each assignment’s stage in the modeling process without overwhelming students with overly complex titles.

Figure 1. The revised prescriptive analytics modeling format and goals emphasize the modeling process and are split into two submissions with interim instructor feedback.

To enhance student learning, we divided the homework assignments into two submissions with an interim instructor assessment. For instance, in one assignment where students had to match project managers to various projects, they first reviewed fictional emails presenting the problem, constraints, and necessary data. After completing their project’s initial setup, students received individualized instructor feedback that encouraged them to think critically about how they approached the problem and the assumptions they made before moving on to analyze their results. This opportunity for early intervention provided support, reduced confusion, and built students’ confidence to better prepare them for creating and running their spreadsheet models. Overall, this two-step approach fostered deeper thinking and engagement with the instructional material and interpreted the outcomes through additional questions and presentations.

To support student learning and prepare them for the graded modeling assignments, we introduced each module with a sequence of low-stakes, formative quizzes built around simplified case-based scenarios using business-style communication such as mock emails and voicemails. These activities helped students build familiarity with core modeling concepts and decision-making logic before tackling more complex tasks.

Graded modeling assignments were framed using business-style communications such as mock emails and memos to reinforce real-world relevance. For example, in Model 4, students received a series of emails outlining a realistic assignment problem, including objectives, constraints, and relevant data. Students submitted their Model 4 Setup individually and received instructor feedback before advancing to the Model 4 Insights phase, where they used Excel to build an optimization model, interpret the results, and respond to scenario-specific questions in a timed quiz. A short video presentation accompanied their submissions to explain their recommendations and rationale. Excel was selected as the modeling platform because it is freely accessible to our students and includes Solver functionality. This two-phase assignment format, supported by structured templates and clearly defined expectations, offered early guidance, encouraged iterative learning, and promoted deeper engagement with course concepts while supporting individual accountability.

The results were clear when comparing course participation before and after implementing the new approach. Submission rates for all modeling assignments increased by 18 percentage points. In addition to higher completion rates, students demonstrated measurable improvements in problem formulation, critical thinking, and communication skills. These changes aligned closely with the course learning outcomes and were reinforced through targeted quiz prompts and structured instructor feedback.

Most notably, engagement in the “new approach course” extended beyond basic requirements. While at least one sensitivity analysis was required, nearly half of the students voluntarily completed an additional, optional analysis. Given how difficult it can be to motivate students to complete even required coursework, this level of voluntary participation highlights the power of purposeful assignment design to drive deeper student investment.

Based on this redesign experience, we recommend several strategies for instructors and instructional designers aiming to create more engaging, authentic assignments. These include using purposeful titles, scaffolding complex tasks, framing assignments in real-world contexts, and integrating tools that support communication and feedback.

Table 1 provides an overview of these strategies and design recommendations:

Strategy	Example	Purpose
Descriptive Titles	“Model Setup 4” “Model Insights 4”	Clarifies the purpose and phase of work
Scaffolding	Two-part submissions with feedback	Reduces cognitive overload, reinforces learning
Real-World Framing	Case-based prompts, mock business emails	Builds relevance and engagement
Technology Integration	Spreadsheet model, video reflection	Supports student modeling practice, professional communication of a problem statement, solution approach, and recommendation with diverse student expression

By renaming assignments, scaffolding tasks, incorporating real-world scenarios, and integrating student-centered feedback mechanisms, we helped students move from performative task completion to meaningful, process-oriented learning. Our collaboration demonstrated that intentional course design, grounded in adult learning principles, can make assignments more than a grade. It can make them matter.

Most importantly, these strategies are highly transferable. While our work was rooted in a prescriptive analytics course, the principles of clear purpose, scaffolded structure, and authentic engagement can be applied across disciplines and modalities. Faculty and instructional designers can adapt these approaches for various disciplines in the humanities, sciences, or professional programs to support meaningful learning.

This redesign also reinforces the value of partnership between instructors and instructional designers. Through open collaboration, we combined disciplinary expertise with design thinking to create a learning experience that was more intentional, more engaging, and ultimately, more effective.

Salina Randall, MEd, is the Associate Director of the Center for Teaching, Learning, and Technology at the University of West Florida. She leads initiatives focused on instructional design, faculty development, and digital accessibility. With over a decade of experience in higher education, she supports faculty in designing inclusive, engaging, and data-informed learning experiences. Her work spans course design, accessibility compliance, and strategic communication, with a particular emphasis on empowering faculty through collaboration and practical solutions.

Dr. Julie Ann Stuart Williams is a Professor of Business Administration at the University of West Florida. Her research spans operations management and business education, with recent work focusing on improving business student writing and modeling-based learning. She has co-authored papers with students in journals such as the European Journal of Operational Research and Business and Professional Communication Quarterly. A recipient of UWF’s Faculty Excellence in Teaching Award, Dr. Williams also serves on the editorial board of INFORMS Transactions on Education and as 2nd Vice Chair of the INFORMS Committee on Teaching and Learning. She holds a Ph.D. in Industrial and Systems Engineering from Georgia Tech.

Source link

September 8, 2025

LAVC Media Arts Faculty Stripped of Administrative Roles Amid Fraud Scandal (LACCD Whistleblower)
Faculty members in the Los Angeles Valley College (LAVC) Media Arts Department implicated in decades of fraud and misconduct have been removed from administrative positions, though they remain in teaching roles.

Over the summer, longtime department head Eric Swelstad, who had led Media Arts since 2008, was replaced as chair by Chad Sustin, a full-time professor of Cinema and Media Arts. The change followed a notification from the LACCD Whistleblower Movement to new Chancellor Alberto J. Roman, alleging that Swelstad falsely claimed membership in the Writers Guild of America – West for more than 20 years and used this misrepresentation in official LACCD promotional materials.

Sustin, a tenured faculty member since 2016 and a former Technicolor post-producer, now leads the department.

The reshuffling comes amid years of internal turmoil. In 2022, full-time cinema professor Arantxa Rodriguez resigned and was replaced by Jonathan Burnett as assistant professor. Rodriguez had previously been implicated in department infighting and, alongside Swelstad, was named as a co-defendant in a 2008 case alleging failure to provide advertised technical training and education. Burnett’s hiring bypassed longtime adjunct and former grant director Dan Watanabe.

Watanabe previously administered several Media Arts training grants, the last of which—ICT & Digital Media, LA RDSN—was reported as fraudulent in 2016. The 2013 grant proposal promised courses such as The Business of Entertainment, Advanced Digital Editing, Photoshop, and After Effects. Yet once funding was approved, The Business of Entertainment and Advanced Digital Editing were archived by LAVC’s Academic Curriculum Committee and Senate. Photoshop and After Effects were offered only minimally, with After Effects disappearing after 2015 and Photoshop shifting to online-only by 2017.

Students reported the suspected fraud to the State of California in 2016, prompting a review of the grant. Renewal applications submitted by Watanabe in 2018 and 2021 were both denied.

Grant Record (Denied Renewal, 2018):
- Project Title: ICT & Digital Media – LA RDSN (Renewal)
- Funding Agency: CCCCO EWD
- Grant Amount: $165,000
- Funding Period: Oct. 1, 2018 – June 30, 2019
- Project Director: Dan Watanabe
- Description: Proposed renewal of the Deputy Sector Navigator grant under the California Community Colleges Chancellor’s Office, focused on curriculum development and alignment with universities and K–12 schools.
https://services.laccd.edu/districtsite/Accreditation/lavc/Standard%20IVA/IVA1-02_Grants_History.pdf

Despite this, Watanabe (who was also passed over for a full-time position at Los Angeles Pierce College) remains an adjunct faculty member slated to teach Cinema 111, Developing Movies – a field he reportedly last worked in twenty years ago. Arantxa Rodriguez and Eric Swelstad have both been scheduled to teach Fall 2025. Despite falsifying his credentials as a member of the Writer’s Guild of America – West, and implying he was a Primetime Emmy Winner (he in fact was the director of a movie that received a local Los Angeles Emmy in the 1990s), he is slated to teach Cinema 101 and screenwriting core class Media Arts 129. Rodriguez will a remote History of Film Class.

Reportedly the new full-time faculty in the department have started working to reverse the damage. Fall 2025 schedule includes Media Arts 112, Creative Sound Design Workshop.
Source link
September 5, 2025
From Policing to Pedagogy: Navigating AI’s Transformative Power – Faculty Focus

The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.

The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.

Why Students Turn to AI

When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.

When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.

The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.

Process Over Product

If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.

In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.

Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.

Disciplinary Innovation

While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.

Creative Disciplines

In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.

Simulating Complexity

In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).

Modeling Scientific Reasoning

In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.

Building AI Fluency Across the Curriculum

To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).

Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.

Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).

Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.

The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.

The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

References

Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.

Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.

Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.

Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.

Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.

Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.

Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.

Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.

McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.

McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.

Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).

National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.

Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.

Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.

Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.

Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.

Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.

Source link

September 5, 2025
From Policing to Pedagogy: Navigating AI’s Transformative Power – Faculty Focus

The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.

The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.

Why Students Turn to AI

When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.

When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.

The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.

Process Over Product

If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.

In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.

Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.

Disciplinary Innovation

While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.

Creative Disciplines

In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.

Simulating Complexity

In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).

Modeling Scientific Reasoning

In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.

Building AI Fluency Across the Curriculum

To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).

Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.

Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).

Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.

The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.

The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

References

Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.

Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.

Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.

Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.

Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.

Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.

Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.

Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.

McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.

McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.

Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).

National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.

Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.

Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.

Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.

Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.

Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.

Source link

September 5, 2025

Tag: Faculty

Survey Says

Teaching, Learning, and Engagement Strategies

Assess Students’ Readiness for Research

Model Behavior and Coach for Success

Introduce and Teach Project Management Skills

Create a Safe Space for New Experiences and Growth

1. Perceiving

2. Understanding

3. Reasoning

4. Executing

5. Creating

Why This Matters

Empathize with Shy Students in the Classroom

Class Participation

Power Dynamics in the Classroom

Not Knowing the Unwritten Rules of College

Being Different, Blazing New Trails

Early Advice that All College Students Need to Hear

Protect Your Grade Point Average (GPA)

Get Noticed in the Classroom. Instructors Write References.

Refine Essential Skills

Find a Mentor to Guide You

Active Outreach Facilitates Student Development and Supports Retention

Step 1: Students describe how they used GenAI, according to our AI scale

Step 2: Students defend their use or non-use of GenAI

Final Thoughts

Why Students Turn to AI

Process Over Product

Disciplinary Innovation

Creative Disciplines

Simulating Complexity

Modeling Scientific Reasoning

Building AI Fluency Across the Curriculum