Since 2022, the integration of generative AI tools into higher education has posed a growing threat to labor stability, particularly for graduate student workers and non-tenure track (NTT) faculty. These technologies are not merely tools for streamlining administrative tasks—they are reshaping the very nature of academic labor. As faculty and administrators increasingly adopt AI-driven technologies, tasks traditionally performed by educators, such as grading, syllabus creation, and feedback, are becoming automated. This shift amplifies the already precarious conditions of academic labor under neoliberal capitalism, where cost-cutting and “efficiency” often supersede the value of expertise.
While generative AI has the potential to continue to support educators in their work, it simultaneously deepens labor precarity for those who occupy the most vulnerable positions in academia. Graduate students and NTT faculty are disproportionately affected as administrators look to generative AI to reduce instructional labor costs, which threatens job security and diminishes opportunities for professional development. However, digital tools—when leveraged strategically—can also provide a means for labor organizing, allowing graduate students and NTT faculty to resist AI-driven deskilling. By focusing on the dual role of digital technologies as both enablers of surveillance and platforms of resistance, this article offers practical strategies for labor organizing in an AI-driven academic environment.
The intersection of university labor and digital humanities provides a connection for exploring the dynamics of organizing and resistance among graduate students and non-tenure track (NTT) faculty. Digital humanities inherently leverage digital tools and platforms for research, teaching, collaboration, and networking. Significantly, these tools—like social media and collaborative software—can facilitate organizing among graduate students and NTT faculty, where individuals can share experiences, disseminate information, and mobilize. These technologies enable participation in the mutually constitutive, technosocial environments of people, devices, and systems exchanging, influencing, and interacting with each other (Wernimont and Losh 2018). Conversely, the same technologies engaging in market-like behaviors can replicate academic capitalism, as neoliberal policies in higher education place pressure on faculty to secure extramural funding, produce marketable research, and engage in entrepreneurial activities. Such conditions lead to precarious employment conditions for NTT faculty, given the emphasis on bringing revenue to the university, and thus raise concerns about the profitability of neoliberalism over teaching, scholarly values of knowledge dissemination, and academic freedom (Cantwell and Kauppinen 2014). The dynamic, networked layers of economics and embodiment with digital humanities technology often emphasize interdisciplinary and collaborative approaches (Burdick et al. 2012), which align with labor organizing.
Digital humanities projects may bring scholars/teachers from different disciplines together to work on complex questions and innovative methods and tools for analyzing and presenting solutions to myriad sociocultural domains within and outside academia (e.g., see Gold and Klein 2023). Similarly, labor organizing efforts, particularly among graduate students and NTT faculty, benefit from interdisciplinary, collaborative, and networked approaches. These efforts often require coordination among academic departments, programs, unions, and advocacy groups to address fair wages, benefits, job security, and working conditions. Digital platforms facilitate knowledge sharing, networking, and mobilization among dispersed individuals and groups and help participants overcome geographical barriers to build solidarity.
The transformation of labor dynamics, mainly due to digital technologies, surveillance, and privacy in academics, presents myriad challenges and opportunities for graduate students and NTT faculty. While digital tools offer avenues for collective organizing and resistance, they also bring forth ethical considerations and potential constraints. For instance, overreliance on non-encrypted and public online posts may exacerbate surveillance and privacy issues. Additionally, the automation of tasks through generative AI threatens deskilling academics and potentially diminishes the value of human expertise. These tensions highlight the need for carefully crafted strategies that leverage digital tools for collective action and safeguard against the threats posed by surveillance and automation. This article begins by examining the impact of digital technologies on labor dynamics in academia, followed by an analysis of the dual role of generative AI as both a threat to academic labor and a tool for organizing. It concludes with practical strategies for resisting the deskilling effects of AI while building solidarity among graduate students and NTT faculty.
Digital Tools | Digital Humanities
Universities are increasingly outsourcing educational functions to tech companies, a trend that affects the quality of learning and directly impacts labor conditions for graduate students and NTT faculty. As contracts with tech companies expand, so does the automation of essential teaching tasks. AI-driven technologies such as adaptive learning systems, automated grading platforms, and content development tools are becoming more prevalent, reducing the need for human instructors. This shift aligns with neoliberal university policies that prioritize profitability over educational values, exacerbating job precarity for those already marginalized within academia.
At the same time as this outsourcing, digital humanities researchers and teachers have realized the gains in fostering interdisciplinary collaboration and expanding access to resources. As Matthew K. Gold and Lauren F. Klein (2019) remind us, digital humanities must extend beyond the academy and work within societal problems to uncover unethical and harmful technologies at the intersection of social justice and society. Part of that work includes examining and using digital humanities tools to discuss generative AI and provide practical strategies for labor organizing and resistance.
While some digital tools pose significant challenges to labor security, they also offer possibilities for resistance. Generative AI and other technologies—though often deployed to deskill academic workers—can also be leveraged by these same workers to organize and resist their exploitation. For example, Slack, Discord, and Mastodon enable dispersed graduate students and NTT faculty to build solidarity, share organizing strategies, and advocate for improved working conditions. Digital petitions and open-access platforms can mobilize support quickly and effectively. By harnessing these tools for organizing purposes, labor activists can counter the dehumanizing effects of generative AI and push for policies that protect human labor in the university.
To mitigate the deskilling effects of generative AI, graduate student workers, and NTT faculty must adopt proactive strategies that leverage digital tools while protecting against surveillance. This includes using encrypted communication platforms like Signal or ProtonMail to secure sensitive conversations and forming cross-campus alliances that amplify organizing efforts. Additionally, graduate students and NTT faculty should push for the inclusion of anti-AI clauses in union contracts, ensuring that AI technologies cannot be used to replace human labor in instructional roles. Ultimately, digital tools must be used strategically to resist the encroachment of AI on academic labor while fostering solidarity and collective action through digital labor.
Digital Labor
Understanding the broader implications of digital labor in academia is essential, particularly its precarious nature for graduate students and NTT faculty. Digital labor refers to activities supporting, maintaining, and developing digital infrastructures, often without proper recognition or compensation. Christian Fuchs (2016) argues that digital labor—whether paid, unpaid, or freelance—is often a form of exploitation, mirroring gig economy trends where precarious workers are underpaid and overworked. This aligns with Kylie Jarrett’s (2021) assertion that certain forms of freelance digital labor blur the boundaries of traditional labor exploitation within capitalist structures.
In academia, graduate students and NTT faculty frequently take on uncompensated digital tasks, such as creating course materials or managing learning management systems. This labor is often invisible but critical to the functioning of the university. For example, universities may hire faculty or graduate assistants at the start of a semester, leaving no time to develop course content, resulting in unpaid work outside their contracts. The expectation that academic workers master and integrate digital tools without institutional support exacerbates precarity. It mirrors broader trends in the gig economy, where maximum productivity is extracted with minimal investment in workers (Giroux 2014).
This precarious digital labor also intersects with the increasing use of generative AI in educational contexts. Amanda Phillips et al. (2018) argue that academic capitalism accelerates labor inequalities by relying on temporary, under-supported workers to sustain digital projects. Graduate students and NTT faculty, who already face precarious working conditions, must often use various digital tools without adequate support or training, leading to further inequalities within higher education.
However, this digital labor—despite its challenges—can also be leveraged for organizing. Understanding the digital infrastructures that sustain academia enables labor organizers to build solidarity, create secure communication channels, and push for institutional changes that recognize and compensate for digital tasks. Practical strategies include advocating for compensation for digital labor, securing provisions in union contracts that protect against deskilling from AI, and using digital tools to enhance organizing efforts. By addressing the exploitation inherent in digital labor, organizers can turn these challenges into opportunities for collective action and better working conditions.
While digital labor often contributes to the exploitation of graduate students and NTT faculty, it also provides pathways for organizing and improving labor conditions. Engaging with digital tools strategically can help organizers resist precarity due to generative AI and the broader neoliberal university structure.
Fears of Job Instability Due to Generative AI
Generative AI tools, such as automated grading systems and adaptive learning technologies, are already being piloted in universities across the U.S., heightening concerns about job replacement. For instance, Arizona State University has integrated AI-driven platforms for students to use chatbots and “teaching and learning tools that could enhance the faculty and teaching experience or the student experience directly in classes” (Bras), which may lead to misunderstood or real concerns about the reduction of graduate student assistants and NTT faculty in courses. When automation begins to replace instructional labor, particularly in large lecture-based courses, where AI can handle grading tasks at scale, it diminishes the need for human educators, leading to concerns about job insecurity and deskilling.
From the general fear of job loss, it is essential to recognize that generative AI’s educational capabilities extend chatbots and grading. Tools like adaptive learning technologies can develop and update course materials, furthering concerns about desikilling. However, as Luckin et al. (2016) assert, generative AI often lacks the nuanced understanding that human graders bring, potentially reducing the quality of feedback to students. At the same time, understanding generative AI’s limitations may not do much to quell concerns about reducing human expertise and reliance upon AI-generated content, grading, and feedback by universities and colleges under financial strain.
Navigating the Intersection of Technology and Labor Dynamics
The implications of generative AI adoption go beyond the automation of tasks, touching on broader labor dynamics within higher education. While generative AI promises efficiency, its use must be examined critically to prevent job displacement and to safeguard the critical roles that graduate students and NTT faculty play in educational mentorship, feedback, and emotional support. As both the U.S. Department of Education (2023) and Rachelee Dené Poth (2023) present, AI tools benefit educators by becoming assistants for them, who develop time-consuming activities for educational outcomes. AI tools cannot replace the human elements of teaching—like mentorship and a deep understanding of student needs—essential for a comprehensive educational experience.
This brings us to the critical intersection between the advancement of technology and labor. The fear of job deskilling is part of a broader tension between adopting advanced technologies and preserving the value of human educators. Universities must embrace new technologies thoughtfully and ensure that labor rights and roles are not diminished. Proactive measures, such as securing union contracts that protect against AI-driven job replacement, ensuring that AI tools complement rather than replace instructional roles, and developing collaborative digital strategies among faculty and graduate students, are essential to safeguarding academic labor. These strategies underscore the need for a collective response that leverages digital tools to resist the commodification of academic labor while fostering solidarity in a rapidly evolving technological landscape.
Leveraging Digital Tools for Collective Action
The use of technology in higher education is about enhancing the experience of learning and can also be about resistance. The 2020 wildcat strike at the University of California, Santa Cruz serves as an example of how digital tools can be used for labor organizing. Despite heavy surveillance, graduate students effectively leveraged social media to mobilize support and advocate for better working conditions (Karlis 2020). This dual nature of digital tools is critical to understand—while they can facilitate surveillance, they can also provide avenues for resistance and organizing.
Encrypted communication technologies like Signal, ProtonMail, and VPNs are vital for ensuring the privacy of organizing efforts, as Estrellado (2023) emphasizes. These tools allow labor organizers to avoid administrative reprisal, ensuring that communication remains secure and confidential. By using these technologies, graduate students and NTT faculty can mitigate the risks posed by surveillance and protect their efforts to resist AI-driven job displacement.
Additionally, labor organizers can look to Arizona State University’s efforts to create chatbots for students to ask questions that help free up time for university staff. Enterprising labor organizers can develop chatbots for organizing, including links to pertinent information such as union contracts, organizing guidelines, dos and don’ts, and upcoming events. These chatbots could be designed to answer frequently asked questions, direct union members to secure communication platforms, and provide real-time updates on labor actions or meetings. Furthermore, by creating chatbots, organizers can empower dispersed and often isolated faculty and graduate students to stay informed and mobilized. Such an innovation can streamline organizing efforts, foster greater participation, and ensure essential and accurate information is available to all members at any time.
Strategizing for Future Labor Movements
In addition to developing generative AI chatbots, labor organizers must continue to leverage other digital tools while being mindful of the risks involved in their use. Social media platforms, online forums, and collaborative tools such as Slack, Discord, Reddit, and Mastodon can be harnessed to create virtual communities where graduate students and NTT faculty can share experiences, strategies, and resources. However, due to the plethora of collaborative tools, co-locating an organizational strategy for one single tool across institutions may prove complex and unwieldy.
Social media platforms can enable rapid dissemination of information and coordination of collective actions, especially seen in the platform formerly known as Twitter, in activism. Yarimar Bonilla and Jonathan Rosa (2015) explore why Twitter became a vital activist platform for "racial inequity, state violence, and media representations" (5)—the community network of multiple streams and views into worldwide events helped to document and contest injustices. Due to changes in X from Twitter and the fragmentation of its user base, hallmarks of social justice campaigns, including documentation in real-life community gatherings, petition and letter-writing, and real-life and virtual protests, risk less participation among interested individuals who no longer participate in the platform. While the site formally known as Twitter offered a promising platform to build solidarity for graduate students and NTT, labor organizers must look to different digital tools.
Searching for digital tools to sustain and grow labor organizing efforts (while crucial for solidarity, documentation, and action) needs to occur with a larger aim in mind. What is the outcome the organizers want? Based on the response, keeping a digital network internal to organizers within an institution may make sense, or operationalizing externally through access to multiple digital media platforms where graduate students and NTT faculty can connect. What may be helpful for labor organizers to consider are multiple websites or chatbots listing links to various platforms for labor organizations. These websites or chatbots may be organized by discipline/field, institution, geographic location, or some other method. For example, many disciplines/fields have websites listing journals and book publishers. Following those websites, organizers could also develop sites with a larger purpose and outcome in mind.
Commodification and Surveillance of Online Spaces
While digital organizing is an optimal information stream for connecting people from multiple locations, it has constraints that can harm potential labor movements. When using digital tools, the commodification of online spaces, managerial surveillance, and the co-opting of labor organizing must be at the forefront of the thoughts of graduate students and NTT faculty.
First, the commodification of online spaces means users must abide by the terms of conditions and privacy policies of each online space, which will likely monetize and sell the personal data of its user base under what Shoshana Zuboff (2019) calls "surveillance capitalism." Her model of economic activity describes the extraction and subsequent analysis of large data from users' online interactions, energizing a profit stream for tech companies. The implications for labor organizing are thus significant: as users' activities are tracked and analyzed, there is a risk that sensitive organizing activities could be exposed (through a hack) or manipulated. Additionally, the terms and conditions of online platforms can limit the types of activities allowed, potentially stifling organizing efforts (Fuchs 2013). For example, platforms like Facebook and Instagram have come under criticism for censoring or de-platforming activist content, thereby hindering the ability of groups to mobilize and advocate for their rights (Tufekci 2017). Moreover, as Tarleton Gillespie (2018) puts forward, the centralized nature of social media platforms means that decisions about content moderation and data privacy happen by a few power entities, often without transparency or accountability to the public. Labor organizers must meet the challenges of each platform's terms and conditions and privacy policies to ensure effective and secure mobilization.
Second, pervasive surveillance by university administrators, faculty looking to undermine labor organization efforts, and external entities such as elected officials in local, state, and federal governments will necessitate a critical understanding of surveillant means in digital labor. Organizers may look at how workers across industries experienced surveillance in the workplace, as seen in the work of Ifeoma Ajunwa, Kate Crawford, and Jason Shultz (2017). For example, the authors adapt Kirstie Ball's (2010) Workplace Surveillance: An Overview to show how employers often look beyond the typical productivity tracking common in workplaces and move into behaviors and personal characteristics such as interactions with other people while on the job, psychometric testing, genetic testing, and background checks. Additionally, sharing the surveillance tools available, Charlotte Garden (2018) documents the many types of surveillance tools available, from keyloggers, tracking software, closed circuit cameras, and RFI badges to the targeted usage of microphones, infrared sensors, and accelerometers to track location.
While Ball concludes that while the surveillance of employees can be balanced and humane, she also warns of abuses of surveillant actions when managers use the data collected to make adverse employment decisions. Ajunwa, Crawford, and Shultz (2017) go further in their recommendations and consider federal information privacy laws, an Employee Privacy Protection Act, and an Employee Health Information Privacy Act to protect employees. However, the protections suggested do not necessarily intersect directly with the National Labor Relations Act. Garden (2018) documents how the NLRA is not responsive in its current design to the rapid advances in surveillance technology; therefore, Garden recommends that the National Labor Relations Board intervene to protect collective action in the workplace. Although the scholarship points to broad and needed interventions to help protect workers across industries, labor organizers need to turn their attention locally. A critical examination of the types of data institutions collect, how and why it is used, and any means administrators use to chill speech or collective action among graduate students and NTT faculty must occur.
Universities often collect vast amounts of data from their students and faculty, ranging from academic performance and attendance records to more granular data from learning management systems and campus Wi-Fi usage (Williamson 2017). This data can be used to improve educational outcomes and administrative efficiency. However, it also raises significant privacy concerns about how organizations use multiple large data sets, or big data, to make connections that are not otherwise readily apparent (Lyon 2014). However, universities likely do not have the capacity or desire to take behavioral and personal characteristic data along with the standard tracking of technology use of appointees, employees, and students because of the cost involved in developing and maintaining such a networked system. Thus, surveillance will likely come from instances where administrators, non-labor friendly appointees, and employees may surveil through social media and websites.
In a 2022 news report, Victoria Ke Li and Phoebe Brous report on a Dallas Morning News investigation into university use of a software called Social Sentinel (renamed Navigate360 Detect). Through this technology, employers from various industries can monitor student and employee social media accounts for potential threats to self-harm, harmothers, or harm buildings on campus. A spokesperson from the University of California at Los Angeles (UCLA)—a university shown to have entered into a contract with the company from an open records request—stated that the software responds to specific keywords and is not "used to monitor any social media account[s]" (Ke Li and Brous 2022, para. 5); however, it may be assumed that the software scans accounts for the keywords that trigger an alert within the system. Within the same reporting, Ke Li and Brous noted, “The Dallas Morning News investigation found that Detect has presented the service as going beyond its publicly stated purpose, further promoting it to administrators at multiple colleges as a tool to moderate and intercept student protests” (para. 10).
There is a disconnect between what the UCLA spokesperson shared with reporters versus what the Dallas Morning News uncovered. Moreover, while the full capabilities of Navigate360's Detect product have yet to be discovered, the University of California system again used threat response tactics at its Santa Cruz location in 2020. During a Wildcat graduate-student strike, the University of California at Santa Cruz (UCSC) administration used military surveillance from the UCSC Police Department, the Alameda County Sheriff's Office, and the California National Guard to suppress the strike (Karlis 2020).
While the University of California system, in the case of these two campuses, cites safety and security as reasons for implementing such surveillance technologies, the practices of procuring and using this technology raise concerns about privacy and the potential suppression of free speech and labor organizing efforts. As seen with UCSC, the use of advanced military surveillance tactics significantly impacted the ability of graduate students to organize effectively due to fear tactics and escalation of military force. The deployment of tools like Navigate360, despite UCLA's assurance of limited use when the product was named Social Sentinel, underscores the potential for misuse in monitoring students and employees, since the Dallas Morning News uncovered the service promoted far more capabilities to universities that it was seeking to contract with than it publicly discloses. The reality of these surveillance practices demands a critical examination of institutional data policies (or their creation) to ensure such tools do not infringe on the rights of students and faculty and to safeguard the integrity of academic and labor movements.
As digital organizing becomes an increasingly critical tool for labor movements, the commodification and surveillance of online spaces present significant challenges that cannot be ignored. Graduate students and NTT faculty must remain vigilant in how they use digital platforms and in advocating for institutional transparency around data collection and surveillance practices. It is crucial to push for more robust data-privacy policies and collective action safeguards to protect the integrity of academic and labor organizing efforts. The dual risks of surveillance and commodification necessitate a proactive approach, where labor organizers adapt their strategies to resist these constraints and demand accountability from both universities and the platforms on which their movements depend. Only by addressing these systemic threats can labor movements harness the full potential of digital tools without compromising their security or autonomy.
Given these risks, labor organizers must adopt secure and strategic methods to protect their activities from surveillance and commodification. The following practical strategies provide ways to leverage digital tools for organizing while mitigating these challenges. By incorporating secure communication channels, building solidarity, and advocating for data privacy protections, graduate students and NTT faculty can resist the adverse effects of digital surveillance and safeguard their organizing efforts.
Practical Strategies
Considering the emergent and uncertain future posed by generative AI and the potential for deskilling among graduate students and NTT faculty, practical strategies for organizing labor as a mode of resistance within university settings and digital spaces are essential. While the U.S. Department of Education and others work to establish generative AI as a partner in education, not a replacement of educators, fears may remain among the most precarious of workers in academia. Therefore, these strategies must address the potential deskilling of academic labor and the broader implications for labor rights and educational integrity:
- Leverage digital platforms: Social media platforms and digital tools such as Discord, Mastodon, and Slack can facilitate the creation of virtual communities for graduate students and NTT faculty looking to build digital solidarity and community. In using the platforms, the facilitators must critically examine the terms and conditions and privacy policies to learn how the sites will align with the purpose and goals of the virtual community.
- Develop websites with links to active virtual communities: Websites listing links to password-protected websites or general information and resources for labor organizing will help to facilitate access for a broad range of academics looking for specific communities based on discipline, labor union, or geographic location. Website developers must establish what will be placed on websites and why through an ethical lens of consent practices.
- Create generative AI chatbots: Use chatbots as a centralized site for FAQs, links to union contracts, organizing guidelines, and updates on upcoming events. These chatbots can be programmed to answer frequently asked questions about labor rights, provide information on how to get involved in organizing efforts, and direct users to secure communication channels. Chatbots can also facilitate quick dissemination of important announcements, such as meeting times or changes in negotiation statuses. They can be accessible 24/7, supporting members regardless of time zones or schedules.
- Develop digital petitions and campaigns: A hallmark of social media and academic activism is ongoing digital petitions that mobilize broad support quickly and efficiently. The petitions can serve as powerful statements calling attention to and demanding better working conditions, fair wages, and job security.
- Access open records: A critical examination of data collection and usage practices by universities and colleges is imperative to safeguard privacy, prevent the misuse of information, and monitor and suppress labor organizing efforts. For public institutions, use open records requests to learn what contracts (if any) universities and colleges have with Navigate360 or similar companies. Also, request information about how administrators have used social media to monitor any strike or union-organizing activity. Get to know the university's climate through open records requests to understand how information will be used and to what extent.
- Push for a university or college data policy: Labor organizers may want to work with university or college officials on a transparent data policy that describes how the institution will and will not use data in the context of labor organization and strikes.
- Advocate for data policy in union contracts: In the absence of or refusal of institutions to establish a policy, at universities or colleges with existing labor unions, push for a clause in the union contract during open negotiations to protect workers' rights to privacy. Engaging in contract negotiations in this area can mitigate the risks associated with advocacy outside of union contract negotiations that often come when unionized graduate students and NTT faculty speak up about workplace conditions.
- Advocate for an anti-generative AI replacement of human labor in union contracts: Push for specific contract clauses prohibiting replacing graduate student and NTT faculty labor with generative AI technologies. This includes ensuring that AI tools, such as automated grading systems or adaptive learning platforms, are used to support rather than replace human educators. By negotiating protections against AI-driven deskilling, union members can safeguard their roles and maintain the value of human expertise in education.
- Use encrypted communication tools: Graduate students and NTT faculty can use encrypted communication tools such as ProtonMail and Signal to counteract surveillance and protect the privacy of organizing activities (when needed). These tools provide secure channels for discussing sensitive topics and planning actions without the fear of being monitored.
- Form alliances with established unions: For non-unionized graduate students and NTT faculty, forming alliances with established labor unions can provide crucial support and resources for organizing efforts within universities. The American Federation of Teachers (AFT) and the United Auto Workers (UAW) have experience advocating for workers' rights and can offer financial, legal, and strategic assistance. In addition, collaborating with these unions can help amplify the voices of graduate students and NTT faculty, making demands more transparent and harder for university administrators to ignore.
- Develop cross-campus collaborations: Create campus networks to enhance solidarity and collective power. Graduate students and NTT faculty may share successful strategies, offer support, and coordinate actions on a larger scale.
- Facilitate continuous professional development: To counteract the potential deskilling effect of generative AI, universities and colleges should be pushed to invest in continuous professional development for graduate students and NTT faculty. Training in new technologies and digital pedagogy can help workers stay relevant and adapt to changes. In addition, institutions need to provide compensated pre-semester workshops and dedicated technical support staff to help implement and troubleshoot digital tools. Professional development programs can also include workshops and certifications focused on integrating generative AI in pedagogically useful ways that enhance rather than replace human expertise.
The practical strategies outlined for organizing labor as a mode of resistance in academic settings provide a comprehensive approach to addressing the challenges of generative AI and digital labor practices. In response to the potential for deskilling academics due to generative AI, these practical strategies underscore the importance of solidarity, data privacy, and proactive policy advocacy. Leveraging digital platforms and creating virtual communities enables graduate students and NTT faculty to build networks to support and share resources. This approach mirrors successful labor organizing tactics also used in academia, and this approach is essential for maintaining collective strength.
Moreover, being aware of surveillance tactics, advocating for transparent data policies, and providing privacy protection using encrypted communications address some privacy concerns against surveillance intended to suppress labor organizing efforts. More importantly, universities and colleges can proactively address generative AI positively through professional development activities, provide paid compensation, and help all educators adapt to technological advancements.
Through digital tools and collaborative efforts, graduate students and NTT faculty can build solidarity, challenge exploitation, and create new spaces for resistance. As the landscape of academic labor continues to evolve, it is essential to remain adaptable, continually refining strategies and practices to meet the demands of the digital age.
