Teaching and Learning with GenAI: Laying the Groundwork for Faculty Development and Instructor Support in Your Writing Center 

Felicitas Hartung, University of California San Diego
Christine Sharp, University of California San Diego

Introduction

“Let AI be the ground of learning rather than the ceiling.” We could not have asked for a more profound or provocative takeaway from one of the faculty members who responded to our post-session survey on our Generative AI (GenAI) “Teaching Chats” during the 2023-2024 academic year [1]. Our respondent’s pithy assessment of the current moment in higher education is an apt description of where writing centers, like our own, are now situated: we are considering new and shifting terrain and imagining how we might advance across this terrain with stability, sustainability, and flexibility in mind. Based on our recent experience incorporating GenAI readiness programming for instructors into our services, we have come to understand that writing centers should adapt the aforementioned adage to establish instructor learning as the “ground” for our services.

Such recalibration, we argue, is one important lever that writing centers should activate to enact lasting change in our institutional writing ecosystems. It allows us to position ourselves as necessary, facilitative partners in institution-spanning conversations related to writing pedagogy and GenAI technologies. Our dynamic and ever-shifting “GenAI moment” offers writing centers an opportunity to encourage instructors to develop and deploy inclusive, student-centered teaching–the kind of teaching that has proven to make a positive and lasting impact upon our students’ learning and which has, in turn, the best chance of closing opportunity gaps in our institutions (Stanford, 2024). [2] By getting instructors ready to teach AI literacy in their courses–a practice known as AI readiness [3]–writing centers can reposition instructors as learners. In so doing, instructors (re)experience the sensation of developing from novice learners to expert teachers, whereby their experience of learner-centered environments makes it more likely that their pedagogical approaches will change in positive ways to align with student-centered approaches. The ensuing mindset shift can uplift an entire teaching and learning ecosystem and (re)establish writing centers as key partners in promoting transformative, student-centered, and inclusive approaches to teaching. 

To promote this important institutional shift at the University of California San Diego (UCSD), members of our unit–Writing and Communication Learning Services (WCLS) at the Teaching and Learning Commons (TLC)–initiated a cross-campus partnership with colleagues in Engaged Teaching Services (ETS) and the Academic Integrity Office (AIO) to advance instructor AI readiness. Absent a unified, institution-wide AI policy, we believed that this kind of highly-collaborative, collective, and co-facilitated faculty development approach would prove most effective in providing individualized support while furthering institutional conversations about AI use. It is worth noting that we intentionally sought out partnerships from units with whom we had previously built trusting relationships and who similarly understood instructor development as the keystone for student-centered education.

It was vital for our units to work closely in community on this programming: both ETS and AIO had established a trusting relationship with our faculty, having shored up their reputation for promoting excellence in faculty learning during the pandemic shift to online teaching, while we served as experts in writing studies. Our extended team depended upon the overlap in our areas of expertise to bring our instructors leading-edge programming related to teaching and learning with AI. In doing so, we developed a shared philosophy with our colleagues in ETS and AIO which centered around inclusive pedagogical practices and provided the foundation for our intervention. From creation to deployment, we co-authored the overall structure and thematic progression of the workshops and teaching chats to generate individual agendas for each conversation.

Our workshops, combined with our subsequent teaching chats, not only addressed the needs of instructors across the disciplines but also promoted exchange across the hierarchy of teaching positions by inviting faculty, graduate and postgraduate instructors, as well as staff. Learning about our students’ and instructors’ needs from these multiple perspectives helped us understand how we might best serve our campus community in this new age of AI. The conversations we had in all of our AI programs transcended individual classrooms and student-tutor interactions, thereby expanding the conversation across campus to take a more student-centered approach in supporting student AI literacy.

Such interventions can enable all teachers and learners to navigate this new terrain effectively. Most importantly, our program encourages instructors to step back into the role of a learner, which enables them to develop student-centered and inclusive approaches to teaching across the curriculum. Nurturing AI readiness in our instructor populations can promote creative reimaginings of traditional teaching models including deconstructing the teacher-student hierarchy and encouraging a learning-centered mindset.

In other words, nurturing AI readiness in our instructor populations is just as important to a writing center’s mission as our charge to provide student-centered services. Indeed, we drew upon foundational writing center principles related to collaborative, peer-based learning to structure our workshops and “teaching chats,” thereby advancing instructor learning while modeling best practices for inclusive learning. When supporting students in building critical AI literacy skills [4], instructors need to be “AI ready,” that is, prepared to promote critical AI literacy in their learning spaces [5]. Based on the experiences at our home institution we invite writing centers to consider the following two concrete interventions which place instructors in the position of learners and promote faculty readiness for the new challenges of teaching with and alongside AI.

Interventions

Intervention 1: Workshop on Writing Assessments and AI

We are positioned uniquely at the University of California in San Diego–in what is colloquially known as the Writing Hub, though our more formal moniker is Writing and Communication Learning Services (WCLS)–as part of a teaching and learning center constituted of six total “hubs”; our colleagues deliver student and faculty-facing services that support undergraduate learning in STEM courses, undergraduate experiential learning, digital learning endeavors, faculty development, and program assessment. In line with the “writing as a mode of learning” approach, our writing center seeks to “empower […] students to leverage writing as a tool for intellectual growth and professional success” (Writing Hub, 2024; Emig, 1977; Tynjälä, 2001, 8). Importantly, however, the Writing Hub intentionally and strategically includes instructor development in our service portfolio. As such, our center provides support to Senate faculty, teaching faculty, lecturers, and graduate and postgraduate student instructors.

In the Spring of 2023, just as higher ed started to take note of the rise of GenAI technology, graduate students at our institution in particular had to face the new realities of teaching with and alongside AI. Because the bulk of summer instruction is handled by our graduate student population, our first Teaching and Learning with GenAI workshop was developed. Titled “Developing Writing Assessments in the Age of AI Text Generators,” this workshop primarily aimed to address the needs of our graduate instructor population. However, a mix of faculty members, graduate and postdoctoral students, and staff members ultimately attended our session. The goal of the workshop was to provide guidance on how instructors could redesign their writing assignments so that students could still reach their learning goals even if they were to use AI tools in their writing process. This reorientation put instructors in a role of learning experts who brought expertise in the subject-matter and in teaching, yet had little experience in integrating AI into their classroom.

This workshop arose from a collaboration between WCLS and ETH. One graduate consultant from each unit collaborated to design and facilitate the workshop. Together, they drew on their expertise as instructors, writers, and educational developers. The graduate consultants facilitating this workshop expressed confidence in addressing this task for two reasons: first, as everyone was learning how to maneuver the new realities of teaching with and alongside GenAI, there were no experts in the field and, therefore, the sense that all participants were equally positioned in terms of their learning, regardless of their position on campus or disciplinary expertise. Therefore, an important task for the consultants was to make explicit to the participants that the workshop was designed as a space to learn together: that no participant–not even the facilitators–were the ultimate arbiters of AI-related knowledge in this space. Second, because of their position as graduate students, the consultants were concerned that faculty members would not be receptive to their suggestions. Yet, because the consultants did set up the workshop as a space to learn together, this worry proved to be unwarranted. In fact, it provided a professional development opportunity for the graduate consultants who had the chance to engage with more seasoned instructors in a conversation among equals.

The training that the consultants had received was helpful in preparing for the workshop–not just in terms of the workshop content but also in how the consultants facilitated the session. Employing a growth mindset is at the heart of our Center’s consultative work (Dweck 2006). Approaching the attendees of the session with the perception that they are genuinely interested in learning from us created an atmosphere of trust. Similarly, the consultants assumed that the instructors had in-depth knowledge of evidence-based teaching practices which allowed the consultants to frame the changes necessary to adjust to the new realities of GenAI as small changes in already well-established teaching frameworks.

In light of the diverse range of teaching expertise in our participant cohort, we charted a path for our workshop that would reinforce best practices and principles for the teaching of writing. Although AI is challenging traditional perspectives regarding the relationship between writing and learning, it is unlikely to dislodge the fundamental principles of the Universal Design of Learning (UDL) which address the needs of a diverse student body (Burgstahler 2021) and which served as the backbone for our interventions. We provided our participants with the following principles through which to recalibrate their teaching in an advancing age of GenAI:

1. 1. The need for learning outcomes and backward design. Based on Brigg’s constructive alignment approach, writing assignments should not just be busy work but help the students acquire the skills described in the course learning outcomes (Briggs, 1996). Critical AI literacy is a transferable skill which can be developed in any course; its achievement may become one of the course’s learning outcomes [6]. 
   2. The alignment between the assignment question type and Bloom’s taxonomy, which breaks learning down into a hierarchy of cognitive levels (Bloom, 1956; Anderson & Krathwohl, 2011). Closed questions may be used for the lowest cognitive level which is recollection, while diagnostic questions can further revisions, and open-ended questions lead students to produce creative work. AI can be integrated into any cognitive level (Hartung & Hicks, 2024). 
   3. The need to communicate expectations clearly with the students, using rubrics and effective language in assignment prompts. When it comes to designing prompts and rubrics, AI tools can support instructors in their course preparation. Platforms like Teachermatic, School.AI, or Curie can assist instructors in designing assignments and rubrics to effectively communicate expectations with students. 
   4. Understanding writing as a form of learning. We need to teach the writing process and reveal the benefits of writing to the students [7].
   5. Promoting intrinsic motivation so that students will achieve their learning goals regardless of the tools they use when they respond to the prompt (Cao & Dede, 2023). Student-centered approaches will reach the students best as they are drawing on the students’ interests.
   6. Building community among students and with the instructor. Even before the pandemic, scholars of teaching and learning have argued that communication alone does not suffice in promoting learning. Relationship building is equally important (Garrison, 2019). 
   7. Support students in their critical engagement with AI. Students at our institution have repeatedly expressed concerns about privacy agreements and copyright infractions that may come with them using AI. Thus, faculty have increasingly been confronted with responding to these concerns often without explicit institutional guidance. Cross-campus collaborations can fill this void by promoting exchange across the institution while providing individualized support. That is why we decided to extend the workshop invitation to staff and graduate students as well as postgraduate instructors.

4. Intervention 2: Instructor Teaching Chats

In partnership with ETS and AIO–in the fall quarter, immediately following our popular summer workshop–we launched a series of Teaching Chats for instructors. These were lightly themed sessions consisting of a general opening question or topic that shaped our general conversation. The goal of these short, one-hour-long sessions was intentionally broad in scope to accommodate the wide range of perspectives and AI readiness in our faculty population. We advertised our event by emphasizing the importance of inclusive pedagogies: “GenAI technologies…have reinforced what has always been important when it comes to effective teaching: an intentionally well-designed course begins with reflective teaching practice” [8]. We aspired “to create a space for faculty and educators to talk about GenAI and to reflect and explore how it influences teaching and learning in the classroom.” Faculty were encouraged to attend any or all of the “Teaching Chats” that interested them. Topics included:

1. 1. To Use or Not to Use GenAI in the Classroom: Opportunities, Limitations, and Reservations
   2. Clarifying Course Objectives and Value: Maintaining Academic Integrity in the Age of GenAI
   3. Promoting Writing Assignments as Meaningful to the Student’s Voice
   4. How to Use GenAI to Amplify Your Teaching (Parts I + II) 

We next developed and deployed a more formalized approach to these topics, based on a “learning communities” model for faculty education. In our “Introduction to Teaching with GenAI” workshop series, we generated a tightly curated list of topics to explore with our faculty attendees, including:

1. 1. Strategies for Facilitating a Conversation on ChatGPT as Course Policy
   2. ChatGPT: A Tool to Enhance 21st Century Skills
   3. Upgrade Your Assessments for Learning
   4. Upgrade Your Writing Assessments for Learning
   5. Activate Active Learning with GenAI [9]

We reprised these workshops in the winter quarter, though faculty showed significantly less interest in attending these sessions compared to our fall quarter Teaching Chats. The drop in attendance may have been owing to the exigencies of teaching on the more intensive and condensed quarter system which created challenges for incorporating last-minute interventions into winter course offerings. We learned, as a result, that instructors experienced difficulty integrating last-minute changes into their curriculum. Given the fast-paced developments taking place with AI technologies, this is a challenge that all writing centers will face in promoting AI readiness for instructors.

Takeaways

Our primary takeaway from these pilots was that it is vital for writing centers to support a culture of critical AI literacy not just for students but also for instructors; writing centers should facilitate ongoing learning opportunities for our instructors even as we meet the needs of our students. It is similarly vital for writing centers to expand our portfolio of expertise to include Critical AI Literacy so that we might continue addressing our changing curricular ecosystems. The most efficient means of quickly and effectively supporting a learning ecosystem that promotes AI readiness for faculty is to find common ground with campus partners–particularly those already involved in instructor development, such as teaching centers–to generate measurable and meaningful impact at our institutions from the ground up.

Thus far, our assessment efforts have included exit surveys; however, we would like to expand our approach to get a more accurate picture of faculty perceptions of their AI readiness and confidence as we move forward. Such efforts are crucial at institutions like our own where a universal policy related to AI in the curriculum has yet to materialize. Our survey data collected from our interventions identified the “learning edge” for our instructors and points the way to five major intervention areas where writing centers and their campus partners can make significant headway in supporting a robust and sustainable AI readiness program for instructors across the institution:

1. Critical AI literacy and learning for students.

As evidenced by several exit survey responses we received, instructors were motivated to reconsider their teaching strategies in light of imagined shifts in the future of the professions: “how [can we] teach those skills or introduce [students] to the potential of AI in their future careers now?” asked one of our colleagues. Responses frequently referenced a self-assigned responsibility to prepare students for unknown professional futures by supporting critical AI literacy in classrooms. Instructors also acknowledged a lack of AI readiness as a stumbling block.  

2. Ethical use of Generative AI. 

One common refrain during our teaching chats was the inevitable march of AI technologies and the consequent need to teach students how to think and create with integrity. “We have a responsibility as educators to protect academic integrity, so the challenge is, how can we best do that while embracing AI technology in the classroom.” In our guided discussions, we identified concrete integration points for incorporating AI technologies into the curriculum to promote academic integrity, foster accountability, and emphasize its use as a tool for cognitive off-loading rather than as a replacement for active learning.

3. Active and inclusive learning with AI. 

Approximately half of our participants sought to actively incorporate AI tools into their classes. Responses suggested that maintaining student engagement, promoting collaboration, and nurturing community motivated their interest in AI technologies. During these discussions, we found that instructors were also mindful of the ways AI technology should be leveraged to promote inclusive instruction and access to learning. Instructors noted the “importance of including students in the discussion regarding use of AI in general.” These early conversations provided the impetus for instructors to embrace the GenAI revolution and anchor their approaches within inclusive teaching frameworks.

4. AI readiness for instructors.

Instructors’ positions on integrating AI technology into their courses is, at least for now, distributed across two predominant positions: “non-adopters,” or, instructors who prefer to restrict student use of AI technologies, and “adopters,” that is, instructors who are actively engaged in incorporating AI technology into their courses. The latter perspective often takes the form of instructors who are “still looking for ways to teach research and writing skills without allowing students to use AI freely, as a crutch […]” while adopters have more wide-ranging perspectives on AI technology use. Whereas non-adopters tend solely to be interested in crafting policies and assignments that prohibit students from using AI technology, adopters’ AI readiness and interests runs the gamut: “I want to find more ways to have students feel accountable and also teach/help each other” according to one of our participants; another aspires to instruct their students in “Using ChatGPT by practicing asking good questions / making the right prompts to get good answers out, and discussing with other students to see what works well and what doesn’t.” While all of our conversations introduced instructors to inclusive teaching strategies, we found that our programming was most effective when it could address the needs of both non-adopters and adopters alike. We have, subsequently, provided opportunities for both groups of instructors to join us for AI Literacy Coffee Chats. Anecdotal evidence in our local institutional setting suggests that the model for our current intervention may transform non-adopters into adopters at institutions that are more willing to embrace AI tools as technologies for learning. The key to such transformation is to provide learning opportunities to instructors.

5. We are all learners.

The most common position expressed by our instructor community was a sense of uncertainty associated with teaching and learning in uncharted waters. Instructors’ comments grappled with the broader curricular impact that AI technology would have, adaptations and innovations that would characterize learning spaces hereafter, and the steep learning curve for both students and instructors alike in a new age of learning. A general sense of unease permeated many of the responses we gathered from our exit surveys, prompted by the current state of our collective unknowing. “I don’t know if I should even bring up AI in my courses,” pondered one participant. Another suggested that the “lack of clarity from the campus… about what academic standards should be upheld” was challenging. “This subject will have a significant effect on our class management–but no one has a clear approach to dealing with it.” From the classroom all the way to the faculty senate, we are all learning, again.

Teaching and Learning with AI

Our interventions and consequent feedback from instructors demonstrate a clear path upon which writing centers should travel as they grapple with higher education’s changing terrain in the age of Generative AI: writing centers should consider finding common ground with campus partners to integrate instructor AI readiness  into their service portfolios. Doing so will reinforce our commitment to student learning while establishing writing centers as important sites for campuswide innovation at the “learning edge” of our fields.

Based on conversations we have had with instructors at our institution and beyond, we acknowledge that not every instructor is willing to adopt such a learning-centered mindset. We are disheartened when instructors tell us that their solution to the AI revolution is a retreat back to the good old blue book exam. Yet, we see a chance in these disappointing conversations with faculty in which writing centers can play a pivotal role: the fact that instructors who are of this mindset acknowledge that change needs to happen might make them receptive to adopting AI readiness approaches to teaching. Because their willingness or ability to make tremendous changes to their teaching may be limited due to time constraints, large classes to teach, extensive service work, family obligations, or other restraints, it is even more important to build interventions on already existing frameworks and for writing center professionals to work closely with campus administrative entities to build guiding principles for our instructors. Acknowledging instructors’ expertise in their field and their command of evidence-based teaching approaches while suggesting small practical changes is, in our experience, the way to encourage a learning-centered mindset in instructors.

Recent recommendations of the MLA-CCCC joint task force on writing and AI, published in their third working paper, align with our experience: it is imperative to provide opportunities for instructors to actively engage with AI tools and to reflect upon the degree to which AI tools will assist students in meeting learning objectives (MLA-CCCC, 2024). Our findings have demonstrated the need for writing centers to increase their instructor-facing development programming, preferably in collaboration with campus partners who have already established themselves in this role. Through these interventions, we will experience how writing centers can take an active role, as we have been called to do in the past, in transforming institutions through and in partnership with all who identify as learners.

Resources

We are pursuing Creative Commons licensing for our materials in the hope that we might share these documents with our readers via our website: https://writinghub.ucsd.edu/for-educators/index.html.

References

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Anderson, Lorin W., & Krathwohl, David R. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Longman.

Anson, Chris. (2015). Crossing Thresholds: What’s to Know about Writing across the Curriculum. In Linda Adler-Kassner and Elizabeth Wardle (Eds.), Naming What We Know: Threshold Concepts of Writing Studies (pp. 202-219). Utah State University Press. 

Bali, M. (2023, April 1). What I Mean When I Say Critical AI Literacy. Reflecting Allowed. https://blog.mahabali.me/educational-technology-2/what-i-mean-when-i-say-critical-ai-literacy/

Biggs, J. (1996). Enhancing Teaching through Constructive Alignment. Higher Education, 32(3), 347–364. http://www.jstor.org/stable/3448076

Bloom, Benjamin Samuel (Ed.). (1956). Taxonomy of Educational Objectives; the Classification of Educational Goals. Longmans, Green.

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Burgstahler, S. (2021). Universal Design in Education: Principles and Applications | DO-IT. https://www.washington.edu/doit/universal-design-education-principles-and-applications

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Estrem, Heidi. (2015). Writing is a Knowledge-Making Activity. In Linda Adler-Kassner and Elizabeth Wardle (Eds.), Naming What We Know: Threshold Concepts of Writing Studies (pp. 202-219). Utah State University Press.

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Footnotes

1. 1. Our “exit ticket” surveys asked participants the following questions, which we left intentionally broad to capture as much information in the early stages of AI awareness as possible: 1) What did you learn from today’s session? 2) Name something from today’s session that makes it challenging to reimagine your teaching practice. 3) What did you learn today that you could apply to your teaching practice? 4) What else would you like to share about today’s session? ↩
   2. In their mixed-method long-term study, Greg Light, Susanna Calkins, Melissa Luna, and Denise Drane espouse approaches to faculty development that promote student-centered equitable learning practices. These practices, according to their research, can lead to more effective teaching strategies when the FDP emphasizes change towards student-centered teaching approaches (Light et al., 2009). We propose moving this approach one step further by not only supporting student-centered teaching approaches through FDPs but also promoting a mindset that allows faculty to learn with and from their students. ↩
   3. AI readiness is “a contextualized way of helping people to understand AI, in particular, data-driven AI” (Luckin et al., 2022). In our discussion herein, we use “AI readiness” to distinguish instructor preparation related to AI and learning spaces as opposed to “Critical AI literacy” which we apply to student awareness, critical assessment, and application of AI tools to supplement learning. See Bali, 2023.↩
   4. In the context of undergraduate student learning, most scholars see “AI literacy” as referencing a suite of skills that exceed knowing how to use AI technologies. However, a more accurate and expansive view of AI literacy – “critical AI literacy” – includes knowing how to use AI technologies in various contexts and understanding the ethical implications entailed in its use (Ng et al., 2021; MLA-CCCC, 2024; Bali 2023).↩
   5. AI literacy in student-centered learning spaces can include educating students in the areas of prompt engineering, critical thinking with AI, and quantitative reasoning with AI (Vee et al., 2023; Bowen and Watson, 2024). ↩
   6. Just like AI, AI literacy is an evolving concept. By acquiring AI literacy, students understand how AI works and how to effectively use it (Klein, 2023). See also (Fink, 2003). ↩
   7. We will recognize this concept as a threshold concept; in “Writing is a Knowledge-Making Activity,” Heidi Estreem posits that “Understanding and identifying how writing is in itself an act of thinking can help people more intentionally recognize and engage with writing as a creative activity, inextricably linked to thought. We don’t simply think first and then write… We write to think” (Adler-Kassner and Wardle, 19). Chris Anson further reminds us of the importance of this threshold concept to faculty development efforts: “In many WAC programs, leaders strategically begin with a strong focus on writing to learn rather than the production of formal, discipline-based writing” (Anson, 209). ↩
   8. Our colleagues in ETH suggested this approach based on the Teaching for Social Justice framework (Adams & Love 2009; Our Approach 2024). ↩
   9. Templates for our workshops can be found on our website: https://writinghub.ucsd.edu/for-educators/index.html ↩