All About My AI Literacy Course

In my first ACRLog post, Teaching AI as an Anti-AI Librarian, I shared how I was approaching the task of teaching an AI literacy course as an anti-AI librarian. For my final post, I’d like to share a follow-up (which a few people have requested—thank you!) sharing a bit of practical information about how I organized the course and reflecting briefly on my first experience solo teaching a credit course.

Getting Started

My course was LIB 1100 AI, Algorithms, and (Y)our Future, a one-credit elective about AI created by my predecessor. 17 students enrolled, and we met twice a week for 50 minutes during the second half of the semester. Because this was a one-credit elective, I knew I couldn’t treat it like a survey course; there just wasn’t time. This course also attracted students from across the university: future designers, teachers, accountants, journalists, lawyers, and more. I wanted the course to be meaningful to everyone, regardless of their disciplinary background or preexisting level of knowledge about AI. For these reasons, I decided to focus on building skills over mastering content.

Before we could begin skill-building, however, I spent the first couple weeks of the course ensuring all students had a common, basic understanding of how AI works, which included in-depth discussions of hallucinations and algorithmic bias. Part of me worried that students might find this phase of the course too elementary. While a few certainly did, I was surprised by the feedback I received.¹ Several students—frequent ChatGPT users—told me they had no idea generative AI tools could produce incorrect or biased information. Others shared that they did not know the term AI included anything other than chatbots; for example, they were shocked to learn how AI technology is involved in facial recognition, dynamic pricing, and resume screening. Most did not know that generative AI works by predicting what is most likely to come next. In general, I found that my students either a) thought they knew more about AI than they did, or b) felt completely lost in AI information overload, unsure how to tell fact from fiction.

As an information literacy librarian, this is where I feel I can make the biggest impact. I want to equip my students with critical thinking, research, and communication skills that will help them thoughtfully and ethically navigate a world being radically reshaped by AI. When they finish my course, I want them to feel empowered to make informed decisions about their personal AI use, investigate AI issues that matter to them, and engage in productive civic dialogue about AI. I believe these skills are more essential to our individual and collective humanity than technology-specific skills like prompt engineering or computer programming. On a practical level, they’re also more evergreen. As AI models evolve faster and faster, technical knowledge about how to engineer prompts or which tool to use quickly becomes outdated. On the other hand, critical thinking skills retain their value as long as we live.

The AI Question

On the first day of class, I laid out these five learning objectives to help set student expectations:

After taking this class, I will be able to . . .

Explain the fundamentals of how AI works.
Evaluate the potential risks and benefits of using AI in different situations.
Understand and analyze multiple perspectives on AI issues.
Discuss AI issues with others.
Independently explore AI issues of interest to me.

You may notice that none of these objectives necessarily involve using AI. I was upfront with students about the fact that I would not ask them to use AI in our classroom. There are already several UNI courses that teach students how to use different AI tools; instead, our class would center around readings and small-group discussions. I expected a few students would drop after hearing this, but none did.

I decided to run a no-AI AI class for a couple of reasons. First, as I’ve already discussed, I truly believe I can best serve my students in an AI-dominated world by helping them develop habits of compassionate communication, curiosity, and critical thinking, particularly when it comes to information and technology issues, and these skills are best developed without relying, in full or in part, on generative AI. Second, I have major ethical concerns with the use of AI in the library classroom, particularly given our field’s values-driven commitments to patron privacy; intellectual freedom; copyright; delivering information with integrity; and opposing racial, gender, and economic inequality. These values are fundamentally incompatible with the design of consumer-focused generative AI tools. Our field has not always acted in line with these values, but I don’t believe generative AI is the solution. I believe it further entrenches inequality—including information inequality. I cannot welcome it in my library classroom.

The Nitty-Gritty

So . . . if students weren’t using AI, what did they actually do during our class meetings?

Since I chose to teach LIB 1100 in-person, I wanted to maximize one of the most important benefits of in-person instruction: the opportunity to have face-to-face discussions. Each week, I assigned a couple of short news articles or videos on AI issues I thought would be relevant to all students, regardless of discipline (e.g. AI in college admissions, Flock cameras, data centers in our state). I chose readings that a) presented multiple points of view on the topic and b) explored both the potential risks and benefits of using AI. When we gathered for class, we discussed the week’s readings in small groups. Throughout the course, we also practiced asking questions about AI, using basic search strategies to find more information, and fact-checking the answers we found. For our last class, students located a news article on an AI topic of their choice and shared their learnings with their peers in a five-minute lightning talk.

Our in-class discussions were highly structured. I hoped this approach would focus student conversation and help the idea of “discussion” feel more approachable for nervous students; generally speaking, I believe this theory was borne out in student feedback. Here are a few methods I used to structure our discussions:

I chose the discussion groups. I tried to place a mix of “strong” and “weak” discussers in each group while still changing the groups every day, so students had the opportunity to hear from all their classmates.
I used preset discussion formats. My favorites are conver-stations, philosophical chairs, and jigsaw, but I also used think-pair-share and snowball.
Discussions consisted of multiple rounds, which were no more than 10 minutes each. Each round had a distinct purpose and built on the previous round.
- For example: The purpose of Round 1 might be “Let’s make sure we’re all on the same page about the facts of this article.” Students define key terms and organize a timeline of events. Round 2 might be “Let’s discuss the risks and benefits of using AI in this way.” Students analyze the different perspectives presented in the article, consider what they would do, and share how the issue could apply to their own lives. Round 3 might be “What should we do in the future?” Students discuss how lawmakers, developers, and leaders in their discipline could respond to the issue.
I provided private, constructive feedback on students’ discussion participation at the midpoint of the course, encouraging good habits and suggesting ways to improve.
Finally, I eased students into the practice of discussion by starting the course with short, low-stakes discussion activities. For example, our first discussion was a think-pair-share in which students brainstormed ideas for our classroom agreement. By the time we began discussing readings in week 2, students were already somewhat familiar with the experience of small-group conversation.

I initially worried students would find the frequent small-group discussions boring. I didn’t use any interactive tools like Mentimeter, Padlet, or Kahoot! (though I’d like to try some this fall). We didn’t play with AI hands-on, for reasons already discussed. We simply sat down in small groups and had highly structured, intentional conversations about AI for two hours a week. When I began receiving student feedback, I was surprised. Students consistently expressed that they enjoyed and looked forward to our in-class discussions; they shared that the structured discussion style helped them learn, they loved getting to hear a variety of opinions from their classmates, and they appreciated the opportunities to direct their own learning.

When I discussed my student feedback with a colleague, she pointed out that many of my students belonged to lecture-heavy disciplines or were at an early, lecture-heavy phase in their college education. This might have been one of their first chances to take a small course in which they could get to know their classmates, share their perspective, and direct their own learning. As I’ve reflected further on LIB 1100, I’ve also begun to wonder if my slower, discussion-focused approach might have resonated with students due to the particular topic of this course.

A Few Final Reflections

AI often feels like something that’s happening to us. You’re told to use AI in your classroom. Suddenly, Flock cameras pop up along your route to work. Every day, we’re buried deeper under an avalanche of stories about job loss, new cybersecurity threats, and environmental devastation. It can feel like everything is spiraling out of control.

But whatever helplessness we might feel about AI, I think our students often feel it more intensely. They are less grounded in the memory of what life was like before 2022; they will graduate into a world already reshaped by AI. Many of them feel there is something wrong with what’s happening, but they also understand there’s no going back. This is the only world they’ll ever know.

In this context, it’s important to carve out space to slow down—especially in the classroom. Students are under immense pressure to “keep up” and “not fall behind,” but in order for them to develop into thoughtful, creative, and compassionate graduates, they need time to breathe. In spite of—or because of—how fast-moving and overwhelming the issue of AI can be, it’s critical that we support students in slowing down, reflecting with intention, developing and sharing their perspectives, and learning from others with grace. Student autonomy in learning matters, especially with an issue like AI, where they may often feel like their autonomy has been stripped away. I believe my students resonated deeply with these aspects of LIB 1100, and I hope I can continue to foster a reflective, generous, and impactful learning environment for my students this fall.

Some Further Reading

If you’re interested in teaching about AI, I hope this post has been useful or thought-provoking in some way. I’m sharing how I approached LIB 1100 because I’ve been asked about this topic quite a bit over the last year, but this is only one teaching method out of many. I’m always learning from other teachers and librarians. To conclude this post, I’d like to share just a few perspectives that have informed my work in the hopes they may be helpful to others as well:

Allison, Leslie, and Tiffany DeRewal. “Where Knowledge Begins? Generative Search, Information Literacy, and the Problem of Friction.” Critical AI 2, no. 2 (2024). https://doi.org/10.1215/2834703X-11556038.

Camarillo, Lauren A. “Squinting Through the Dawn of AI: Embedding Algorithmic Literacy Principles in Library Instruction.” In Democratizing Knowledge + Access + Opportunities: The Proceedings of the ACRL 2025 Conference, edited by Dawn Mueller. Association of College and Research Libraries, 2025. https://www.ala.org/sites/default/files/2025-03/SquintingThroughtheDawnofAI.pdf.

Ellis, Elizabeth and Amanda Kaufman. “Diving Beneath the Surface: Incorporating Critical AI Literacy in Library Instruction.” Presentation, LOEX Conference, Norfolk, VA, May 8, 2026. https://docs.google.com/presentation/d/1R9e1S_x1Mc-0Xl3rz6dcY8nZuPLgvDMcwQDnAmERnvc.

Illingworth, Sam. “What Is Critical AI Literacy?” Slow AI, February 13, 2026. https://theslowai.substack.com/p/what-is-critical-ai-literacy.

TILT Higher Ed. https://www.tilthighered.com/.

Slater, Kay. “Against AI: Critical Refusal in the Library.” Library Trends 73, no. 4 (2025): 588–608. https://doi.org/10.1353/lib.2025.a968497.

Notes

When I refer to “student feedback” throughout this post, I’m aggregating feedback received through in-class minute papers; graded learning reflections; three anonymous Google Forms surveys I administered throughout the course; my final, university-administered student evaluations; and students speaking directly with me. ↩︎

A (Long Overdue) LOEX 2026 Recap

I attended LOEX for the first time this year, and it was a wonderful experience! I returned to UNI with so many new connections across Libraryworld and exciting ideas for my classroom this fall. Today, I’d like to share my takeaways from a few critical conference sessions and take some time to discuss the future of LOEX.

What is AI literacy, anyway?

The session that made the biggest impact on me was “Diving Beneath the Surface: Incorporating Critical AI Literacy in Library Instruction,” presented by Amanda Kaufman and Elizabeth Ellis. Kaufman and Ellis suggest a distinction between “AI literacy” and “critical AI literacy.” They define “AI literacy” as “largely focused on building AI skills,” such as understanding how AI works, engineering effective prompts, and evaluating AI outputs for accuracy. In an AI literacy framework, ethics discussions tend to focus on when it’s okay to use AI and how you should disclose your usage. On the other hand, “critical AI literacy” involves “examining the sociocultural, economic, and political dimensions of how information is mediated on artificial intelligence platforms.” Critical AI literacy learning outcomes include analyzing how generative AI contributes to the spread of misinformation, describing the environmental or labor costs behind AI systems, and evaluating AI outputs for whether (and why!) bias is present.

Kaufman and Ellis articulated a distinction I’ve been attempting to express all year. We’re under increasing pressure to teach AI literacy in higher ed, but I believe that what students most need is critical AI literacy, a framework that engages with AI through questions about bias, autonomy, and human rights. It leaves space for thoughtful AI refusal, rather than taking AI use as a given. This session was jam-packed with practical information like learning outcomes and classroom activities, and I’m looking forward to integrating critical AI literacy into my classroom this fall.

Reading is FUNdamental

Another one of my favorite sessions was “Reading Beneath the Surface: Reimagining Information Literacy Beyond Searching and Evaluating.” In this session, Julia Anderson and Megan Heuer described their research on undergraduate student reading strategies. They found that students often employ ineffective reading strategies when scanning search results for helpful links or skimming articles for key points. They identified a substantial gap between how effectively faculty expect students to be able to read academic literature and how effectively students actually read. They also provided quite a few suggestions for how to integrate reading instruction into both one-shots and credit teaching. For example, perhaps students could practice reading an abstract in small groups or use social annotation tools like Perusall to work through their assigned readings.

Reading instruction has become one of my biggest information literacy interests over the last year. After all, reading is the most fundamental research skill; if you can’t read at grade level, you can’t evaluate arguments, apply concepts, write effectively, or do so many of the other tasks we regularly ask of students. I’ve been thinking a lot this year about what I can do as a liaison librarian to support student success in this area, so I was excited to learn from Anderson and Heuer’s work.

AI in the Classroom

I would be remiss not to mention Nicole Baker and Brigette Meskell’s session, “Humans at the Helm: Teaching in an AI Era.” I did not attend this session, but I certainly heard more buzz about it than any other LOEX session, and I was able to chat briefly with Baker about her programming process afterward. In this session, Baker and Meskell shared a tool called Bloom’s StAIrcase, which they developed to help instructors find and/or generate AI-integrated classroom activities. Referring back to Kaufman and Ellis’s presentation, I would generally classify this as an “AI literacy” tool and not a “critical AI literacy” tool. While I appreciate how these activities emphasize hands-on learning and critiquing the outputs of AI tools, I still feel that the privacy policies of consumer-focused generative AI tools are fundamentally incompatible with librarianship’s core privacy ethics; for this reason (among many others), I still cannot encourage, much less require, the use of AI in my library classroom. However, those who are less skeptical than I am about integrating AI use into classroom assignments may find this tool very useful, and it’s certainly worth discussing in any LOEX 2026 recap.

LOEX in 2027 and Beyond

Many of you have likely heard the news that LOEX 2027 will be held exclusively online, and from 2028 onward, LOEX conferences will alternate between in-person and online formats. In an April email to members, LOEX shared that the reason for this change was to ensure the long-term financial stability of the conference.

There was a lot of discussion about this change at LOEX 2026, and I heard many different opinions. Personally, I’m excited for this change. The cost for me to attend LOEX this year was approximately $2,000, but my annual professional development stipend is $1,000. (I should mention that, in addition to my stipend, Rod Library generously covered $600 of unexpected expenses that arose because my original flights were canceled, but there is no guarantee that this kind of money would be available for similar situations in the future.) Additionally, there is no quick and easy route from Cedar Falls, IA to Norfolk, VA (or to most major cities outside the Midwest). I won’t belabor the point with a play-by-play, but in summary, this three-day conference resulted in a six-day trip, and I was actively traveling for four out of the six days. It was exhausting as it sounds.

I’m still glad I went to LOEX this year, particularly since I’m at such a critical juncture in my career. In general, I’m a proponent of networking, teaching, and dialoguing in-person. It offers unique, spontaneous opportunities for connection that are hard to replicate online, and Zoom fatigue is real. At the same time, I don’t think I would attend LOEX again for the foreseeable future without this change. Online conferences are often more accessible to librarians with disabilities or childcare responsibilities and librarians who live far from major transportation hubs. They’re also more affordable, which is increasingly essential as we all experience funding cuts. In a context where so many of our in-person gathering places have been turned into Zoom breakout rooms, however, I understand why many people I met at the conference were disappointed by this change.

What about you? Please share your thoughts in the comments!

Did you go to LOEX (or any other amazing conferences) in May? What was your favorite session?
What do you think about LOEX’s upcoming format change?

Summer Break Book Recommendations: Critical Pedagogy, AI, and More

With summer break around the corner, I’m eagerly beginning to plan what books I might read this summer (maybe this is the year I’ll finally crack open Emily Wilson’s Iliad . . .). I’m also reflecting on the books I read this year as I prepared to teach my own course, an AI literacy elective, for the first time. Today, as we ease ourselves into summer break, I want to share four quickfire reviews of books I’ve recently read about pedagogy and AI. If you’re looking for something to read this summer, I hope you find something intriguing in this list!

I won’t bury the lede: Pedagogy of Freedom by Paulo Freire is the most impactful book I read this academic year. This book is a necessary antidote to how higher education is currently (de)valued, understood, and discussed, and it helped me refocus my teaching philosophy. Many people argue that politics should be removed from the classroom, but as Freire reminds us, education is inherently political because there can be no democracy without an educated population. While I do think post-graduation outcomes like salary and job placement can be part of how we evaluate a university, particularly when students take on so much debt to acquire a degree, it should not be the end of the conversation. Exemplary institutions, programs, and teachers do as Freire says: foster lifelong learning, develop critical thinking skills, and graduate students who are locally engaged and, yes, tuned in to the injustice around them with the desire, skills, and knowledge to make change.

Next up: The Skillful Teacher by Stephen D. Brookfield (third edition), a recommendation from my wonderful colleague Angie. This is an engaging, down-to-earth guide to teaching, filled with practical tips and examples of assignments, instructor feedback, and rubrics. I found the chapters on critical thinking and classroom discussion particularly useful, and I plan to try several of the discussion activities in my course. I also appreciate how Brookfield grounds his philosophy about teaching college in the context of adult education studies, and I think he has a keen understanding of college students today. This edition was published in 2015, so some material about emerging technologies and teaching online may be outdated; however, it’s still a worthwhile read on the whole, particularly for those seeking concrete tips and examples.

Finally, I read two books about AI (both recommendations from my wonderful colleague Aj). Mastering AI by Jeremy Kahn is an accessible, engaging introduction to AI issues across society, including education, medicine, politics, business, and more. At times, Kahn is more bullish on AI than I would be, but in general, I think he fairly represents and evaluates both AI-critical and AI-optimist viewpoints. This is a book I would share with my mom or another layperson who wants to understand what’s going on with AI but feels overwhelmed trying to follow the news. If an unlimited ebook license were available to us, I would assign excerpts from this book as readings in my course.

Atlas of AI by Kate Crawford is more scholarly than Mastering AI, but for a scholarly text, it falls on the more digestible side. Crawford focuses on making visible the invisibilized human and environmental harms of AI, like the electricity costs of data centers, the brutality behind mining rare earth elements, and the psychological trauma endured by content moderators in the Global South. She effectively punctures the idea of AI as something abstract, far away, “in the cloud,” grounding these tools in human blood and sweat. This is essential reading for those concerned about the planetary and human costs of AI.

Finally, some non-work-related book recommendations to wrap up this post 🙂

I Was the Jukebox by Sandra Beasley
With My Back to the World by Victoria Chang
Ararat by Louise Glück
Small Things Like These by Claire Keegan
Ghosts by Edith Wharton

What have you been reading recently (library-related or not)? What do you plan to read this summer? Please share in the comments below!

How Can We Teach Material We Know Too Well?

This semester, I’ve been participating in a multidisciplinary discussion group with other University of Northern Iowa faculty who teach general education courses. We’re reading How Learning Works by Ambrose et al. (1st edition) and discussing the unique strategies, challenges, and opportunities of teaching gen ed courses.

We recently studied chapter four of How Learning Works, which is about how students develop mastery of new knowledge and skills. I was struck by a case study the authors discussed at the beginning of the chapter. Students performed well on their individual projects in a management course, so the professor assumed that in group projects, they could exchange ideas and do even better. However, the quality of student work declined steeply after they were put in groups.

Ambrose et al. suggest a reason for this. The management professor’s assumption that student work will improve in groups “is predicated on the expectation that students will know how to work effectively in groups. In fact, successful teamwork requires not only content skills and knowledge, but also an additional and qualitatively different set of process skills, such as the ability to delegate tasks, coordinate efforts, resolve conflicts, and synthesize the contributions of group members.” Because we (instructors, librarians, workplace professionals) have long since mastered skills like delegating tasks and coordinating multiple people, these skills become invisible to us. We call upon them without thinking, and we forget how hard it might have been to learn them. We forget we even had to learn them.

Ambrose et al. use the following model, developed by Sprague and Stuart (2000), to illustrate how expertise develops over time:

This basic graphic representation is used in both Ambrose et al. and Sprague and Stuart. The specific image I’m showing is from a presentation done by a group at Carnegie Mellon University, since the image in How Learning Works was very blurry.

Students begin in a state of unconscious incompetence; they don’t know what they don’t know. As they begin to learn, they understand the gaps in their knowledge; this is conscious incompetence. At this stage, they can more effectively direct their own learning. Eventually, they develop conscious competence. They’ve acquired knowledge in their field, but they still need to pause and think before exercising their new knowledge and skills. Finally, they reach the expert stage of unconscious competence, able to instinctively make connections, draw conclusions, and exercise skills. This model can apply to both disciplinary knowledge (How do I catalog a book?) and process skills (How do I work in a group?).

This explains some of the missed connections in the classroom, including the library classroom. Students don’t know what they don’t know, and we’ve forgotten what they don’t know. I began to consider where these gaps might show up in my own work.

Consider the following task: Identify a scholarly information source to support your argument in this paper. This is a task I might give students in a credit-bearing course, demonstrate in a one-shot, or assist them with in a reference consultation. What are some assumptions we (librarians, faculty, instructors) make about students when we assign this task? Here are two I thought of; you might identify more.

We assume students can read scholarly literature. Reading an article in the American Historical Review is very different from reading a high school history textbook. Before we give students their first peer-reviewed article, we often provide scaffolded practice in the relevant disciplinary concepts, but do we provide scaffolded practice in skills like pre-reading, annotating, or summarizing difficult texts? Usually not. In my experience, instructors generally assume that someone else has already taught their students these skills. If one-third of United States twelfth-graders fall below a “basic” proficiency in reading, why do we expect them to be able to fluently read peer-reviewed scholarship by October of their freshman year? Even worse, without dedicated instruction, students may not recognize college-level reading as a distinct set of skills—they don’t know what they don’t know. When they struggle to understand peer-reviewed articles using their toolkit of high school reading skills, they get unnecessarily discouraged. This experience can reinforce a student’s preexisting self-image of “just being bad at reading.”

This issue is part of why students can feel so easily frustrated by scholarly literature. Reading is the most fundamental research skill—a skill that’s so second-nature to librarians and non-library faculty, we’ve forgotten how difficult it can be to acquire. If we don’t go out of our way to help students develop this skill, they’re more likely to quote mine or plagiarize to pass their classes.

We assume students can apply others’ findings to their own work. Ambrose et al. discuss multiple reasons why students fail to transfer knowledge from one context to another. Overspecificity or context dependence is one that particularly resonated with me; I think it explains why students struggle to identify useful scholarly sources for their papers. Overspecificity is when students “associate that knowledge too closely with the context in which they originally learned it and thus [do] not think to apply it—or know how to apply it—outside that context.” They may feel fine answering quiz questions based on the textbook, but identifying and analyzing those same concepts in the abstracts of peer-reviewed literature is another skill entirely.

I’ve particularly noticed this issue in reference consultations. For example, say a student is researching depression in college students. If we find a peer-reviewed article about rates of depression in community college students, the students I work with are likely to say the article is irrelevant to their research question. Not that the article is suboptimal, but that it is completely irrelevant and totally unusable. Why? Because they are not only researching community college students; they are researching all college students. As librarians, we understand how to read a piece of research and mentally map similar situations where the findings could apply; in fact, we do this unconsciously as we read. However, students have not yet learned this skill—and it is a skill they have to learn. Given the highly specific nature of peer-reviewed research, this can lead to them feeling particularly discouraged. They think that there’s “nothing about their topic,” but there’s actually a lot of research already out there!

It seems like we’re stuck in a catch-22: Once we’ve mastered library and research skills, it’s harder to use our expertise to guide others. How can we overcome our unconscious competence to help students learn from square one?

In chapters 4 and 7 of How Learning Works, Ambrose et al. offer a few strategies. Here are three that stood out to me:

Talk to current students, teaching assistants, or recent grads. Since they’re often at an earlier stage of learning mastery (conscious competence, rather than unconscious competence), they may be more aware of each discrete skill that goes into completing a complex task. They’re also more aware of the skill gaps that most frequently impact them and their peers. Their perspective can help us break down our activities into more approachable chunks.

We should be more explicit than we may think is necessary. When I’m demonstrating how to use the library catalog, I try to describe my every move in enough detail that if someone couldn’t see my screen, they could still replicate what I’m doing. I also verbalize my thought process as I walk through the catalog. For example, instead of “Let’s click on this article and read it,” I say something like, “At first glance, I think this article could help answer my question because . . . when I click on the link, I’m first going to check for . . . then, I’m going to open the article by . . . now that I have a PDF open, I’m going to start reading by . . . I still think this looks useful, so I’m going to save it for later using . . .” This demonstrates a discovery thought process, which students may be very unfamiliar with. It helps them connect the dots between the new tool they’re learning, the process of college-level research, and the projects they need to complete.

During hands-on activities, embed opportunities for students to reflect on their own work. What did they do and why? This helps students develop awareness of their own learning strengths, weaknesses, and strategies. When they explain their processes, we can also identify knowledge and skill gaps we might not have anticipated. For example, in a recent first-year writing course, a student shared that they searched “Why do people procrastinate?” in the library catalog and were disappointed by the results. I realized that I’d forgotten to explain that the library catalog is not designed for natural language searching; that knowledge is so second-nature to me, I’d just demonstrated keyword searching without explaining why. I took the opportunity to explain the difference between keyword and natural language searching and demonstrate how to break down a question into keywords, which significantly improved the students’ results.

I anticipate returning to this topic throughout my career, but here’s a complicating thought to close today’s post: How much is it our job as librarians to address these reading, writing, and research skill gaps? Most of us focus our instruction energy on one-shots and reference consultations, not credit-bearing courses. It’s hard for us to address these gaps since skill development requires consistent, scaffolded, guided practice over time. Sometimes, the most effective—and the most difficult—solution is to share our insights with non-library faculty when appropriate.

That being said, I still find Ambrose et al.’s insights useful for identifying pain points in library instruction. Why are students coming (or not coming) to our reference consultations? Why might they struggle with a particular one-shot activity or assignment? I would love to hear from you: How do you identify and address these gaps in student skill development?

Many thanks to Jonathan Chenoweth, Jeremy Schraffenberger, and all the other members of UNIFIed Academy for our wonderful discussions (and delicious snacks)!

I Survived My First Faculty Evaluation (And So Will You)

Like approximately half of all academic librarians, I’m classified as faculty. This year, I went through my first faculty evaluation . . . and our evaluations fall in mid-October, six weeks after I started my position.

It was an intense six weeks.

My portfolio is 80% teaching/librarianship and 20% service. In six weeks, I assembled ten artifacts to represent my teaching, librarianship, and service; wrote a cover sheet for each artifact; wrote my teaching philosophy; and, of course, updated my CV (which it seems like I am never done doing).

When I started this process, I was totally overwhelmed. How could I effectively present my case with only six weeks under my belt? I didn’t even know where all the restrooms were yet! But I just got my evaluation letters back, and I’m happy to say everything turned out well. Today, I want to share the five biggest lessons that helped me succeed during the faculty evaluation process.

1. Stay calm.

This is the most important lesson I learned. Your first faculty evaluation will likely feel overwhelming and stressful, but remember: The librarians evaluating you want you to do well. Seriously! They want you to succeed in your role (otherwise, why would they have hired you?), and evaluation is about recognizing that success.

Since my first evaluation only covered the first six weeks I was in the role, the pressure was even lower. As my faculty mentor said: “Don’t burn down the library, and you’ll be fine.”

2. Start now.

I consistently worked on my portfolio during those six weeks, and I still scrambled to make the deadline. This year, I’m determined to avoid any last-minute panics. Once I turned in my 2025 portfolio, I immediately started a list of items I wanted to include in my 2026 portfolio and jotted down notes for the cover sheets. I’m also trying to be more proactive about keeping my CV up to date. Once the fall semester begins, I’ll block out two hours in my calendar every week to work on my portfolio.

3. Understand your context.

What does success look like in your library, to your professional assessment committee (PAC) and department head? Finding this out will help you establish clear, achievable evaluation goals. Meet with your PAC chair and department head well in advance of any deadlines and ask what their expectations are for you in your first evaluation. Keep an open line of communication going for feedback and questions.

As I spent time co-working with new faculty colleagues across campus who were also preparing their portfolios, I noticed that evaluation procedures and standards varied from department to department. Every department has its own departmental standards and criteria, which explain what “Needs Improvement,” “Meets Expectations,” and “Exceeds Expectations” ratings mean in that particular department. It was important to focus on what my PAC and department head wanted to see from me during my first six weeks, rather than comparing my portfolio to those of my friends in chemistry, education, or business. That could have led to faulty expectations for my rating or to time wasted creating portfolio components I didn’t need.

4. Don’t be afraid to ask for help.

I’m not sure if this is just me, but I have a bit of a complex around the idea of “cheating,” which I think is left over from my undergraduate days. When I was assembling my portfolio, I was afraid to ask for help because I worried it would be considered cheating to ask my colleagues for feedback on my portfolio before I turned it in. But nobody covers this stuff in library school, and you’re never going to learn how to do it if you don’t ask. Plus, as previously stated, your colleagues want you to succeed, not to flounder helplessly in silence.

I found it helpful to explicitly voice this concern, directly asking my PAC co-chairs and other colleagues how it would be acceptable to get feedback before I turned in my materials. To my surprise, everyone was happy to share examples of their own portfolios, answer my formatting questions, and provide feedback on which artifacts I’d chosen.

5. Don’t be afraid to advocate for yourself.

My portfolio is 80% librarianship/teaching and 20% service. However, I also had quite a few scholarship items I wanted to include, including a conference presentation, my tenure guest editing a literary journal last fall, and a handful of creative and critical publications. In my opinion, I would have easily been able to “Meet Expectations” for scholarship if scholarship were part of my portfolio, but it isn’t. This is because my contract is for a three-year term and not a tenure-track position. I knew that my predecessor had been tenured, and the reason I’m not able to be on the tenure track is more a result of budgetary issues than anything else. It felt unfair that my predecessor would have been recognized for the very same work I was doing, but I wouldn’t be recognized due to factors outside my control.

After some hesitation, I decided to bring these concerns to my PAC co-chairs. They encouraged me to include my scholarship in my portfolio, just under the “service” category. In my portfolio documentation, I made the case for how my scholarship could be understood as service and argued that with the combination of scholarship and service I was documenting, I should receive a rating of “Exceeds Expectations” in the service category. But the moment I submitted the portfolio, I felt a rush of embarrassment. I mean, who did I think I was? I’d only been here six weeks, and I thought I should receive “Exceeds Expectations”? I was overwhelmed by the urge to scrub all my scholarship, which surely wasn’t very good anyway, from my portfolio. But I’d already submitted it. There was nothing to do but wait.

As new librarians, it can feel precarious to advocate for ourselves; not only are we new to our institutions, we’re new to the field as a whole. Sometimes, we need to pick our battles, but some battles are totally worth fighting. When I got my evaluation letters back, I found that my PAC gave me an “Exceeds Expectations” rating for service, and my department head gave me the highest level of “Meets Expectations.” I know I would have received lower ratings if I had not advocated for myself. If you notice an element of the evaluation process that feels unfair, don’t be afraid to bring it to your colleagues and suggest a change.

Epilogue: Personal Reflections

In the process of writing this post, I realized something: When I graduated from library school and entered the workforce, I had no idea that I would need to undergo a huge mindset shift regarding the evaluation of my work. I was raised to understand that “going above and beyond” is a success, and “just” doing what you’re supposed to do is a failure of will and intellect. If you’re only “meeting expectations” in the classroom, not exceeding them, are you going to get an A+? Are you going to land a GAship? Are you going to stand out in a brutal job market?

But this mentality just doesn’t translate to a 9-5 job setting—or at least, not to mine. It’s like trying to use a meat tenderizer to prepare spinach or a salad spinner to prepare a steak. I remember how flabbergasted I was when my department head said he expected and wanted me to get a “Meet Expectations” rating. Let’s step back. Doesn’t it make sense that your supervisor wants you to meet their expectations? But I was so identified with the idea that “succeeding = exceeding,” I genuinely couldn’t understand him. What do you mean, you want me to meet expectations? I thought. Isn’t that like getting a C?

That’s what makes this so tricky: Relentlessly obsessing over “going above and beyond” during my MLIS led to me acquiring the experiences and accomplishments that helped me land this job. But now that I have a job, I need to evaluate myself using a different mindset than the one that got me here. It took me months to realize that how success is evaluated in the workplace is different from how success is evaluated at school. Although tedious, the faculty evaluation process was a valuable exercise because it forced me into a new perspective. I had to go point-by-point through the departmental standards and criteria and connect them to the work I’d done, which forced me to see my work through the eyes of my colleagues (librarians with decades of work experience) rather than through my own (heavily biased, for reasons already discussed). More than any of the other lessons I learned during the evaluation process, it’s this mindset shift that will help me succeed in my 2026 faculty evaluation.