From Workload to Wellbeing: Global Insights on AI’s Impact on Education

Framework

This research is guided by the Sociotechnical Systems Theory (STS), which examines the complex interplay between technology and social environments. By applying this framework, the study explores how AI impacts both the technical and social dimensions of education. STS allows for a comprehensive investigation into how AI tools, such as those used for grading and content creation, influence educators' workload and mental health, while also addressing broader ethical concerns like privacy, bias, and equity. By focusing on both the technological and societal implications of AI, the research aims to provide insights into responsible and inclusive AI implementation in educational settings, ensuring that the benefits of AI are balanced with ethical considerations for educators and students alike (Baxter & Sommerville, 2011; Bostrom & Yudkowsky, 2014).

Methods

Research Aim and Questions
As AI continues to permeate educational settings, it becomes imperative to explore its potential ramifications on student and educator well-being. While AI offers advantages such as personalized learning and efficiency, it also presents risks, including concerns about data privacy, algorithmic bias, and its influence on mental health. Therefore, interdisciplinary collaboration is crucial to navigate these complexities, develop ethical guidelines, and ensure the responsible and equitable use of AI across all educational domains (Chiu et al., 2023).
 The objective of this research was to explore educators' perceptions regarding the use of AI in education, focusing on their concerns, perceived benefits, and ethical challenges. The study examined AI’s impact on teaching and learning processes, including its potential to personalize learning, improve efficiency, and pose risks to privacy and mental health. By analyzing how educators integrate AI tools into their teaching, adjust methodologies, and navigate institutional policies, this research contributes to the understanding of AI's emerging role in education. Specifically, the research questions included:
1. How familiar are educators with AI, and how frequently do they use AI tools at home and in the classroom?
2. How do educators adjust their teaching methods in response to students using AI tools for assignments?
3. What training and resources do educators need to effectively integrate AI tools into their teaching?
4. How do institutional policies affect educators' ability to adopt and use AI tools in their teaching?
5. What is the perceived impact of AI on educators' and students' mental health, and what strategies do educators suggest to mitigate any negative effects?

Methodology
The study utilized a mixed-methods approach, combining both quantitative and qualitative data. Data was gathered through an online Qualtrics survey (Qualtrics.com) and included both open and closed questions. Seven of the survey questions examined demographics such as gender, ethnicity, country, level of education, years of experience, type of school, and occupation. Also, the survey included six multiple-choice and five open-ended questions, which included determining educators' familiarity of AI, the extent they perceived students to use AI, usage in work and home settings, specific tools used in the classroom, the level of agreement on AI tools and platforms, school policies regarding AI, the impact of AI on mental health, training needed, curriculum adjustments, and additional suggestions. For recruitment purposes, a script was posted on educational listservs and social media pages and emailed to educators. The research study was approved by the Institutional Review Board at the researchers’ university.

Data Analysis
Quantitative analysis involved descriptive statistics, conducted using Excel and SPSS version 24, to summarize participant demographics such as geographical locations, fields of study, AI familiarity, and AI usage in teaching. Categorical data and 5-point Likert scale items were used to measure these variables, with non-parametric tests (e.g., chi-square) employed to assess differences between groups.

Further, researchers independently and systematically analyze these responses through an inductive and comparative approach. They developed initial codes, created categories, and further refined these into broader themes. To enhance reliability, intercoder agreement is reached through collaborative comparison of coding results. This process ensures a comprehensive understanding of educators’ perspectives on AI integration, including policies and recommendations for improving AI use in educational settings.

Results

Note: The data collection will end on October 1, 2024. The results are yet to be fully analyzed. The following is a partial summary as of 9.26.24.

Participant Demographics
The study was comprised of 119 educators. Eighty-four of the participants (70.6%) were female, while 32 participants (26.9%) were male, and 3 participants (2.5%) identified as other. In terms of ethnicity, 89 participants (74.8%) identified as White or Caucasian, followed by 11 participants (9.2%) who identified as Hispanic or Latino/a, 10 participants (8.4%) as Asian/Asian Indian or Pacific Islander, and 6 participants (5.0%) as Black or African American. Additionally, 3 participants (2.5%) selected Other for their ethnic background. Most participants resided in the United States; however, 22 respondents (18.5%) indicated they were from international locations, including the UK, Italy, Australia, Germany, the Philippines, Nigeria, South Korea, India, Sweden, Brazil, Azerbaijan, Sudan, and Malaysia.

The educators came from diverse backgrounds in terms of teaching experience, types of educational institutions, and professional roles. Regarding teaching experience, 20.2% of participants had between 0-5 years, 19.3% had between 6-10 years, and 17.7% reported 11-15 years of experience. Some of the participants (17.7%) indicated they had over 15 years of teaching experience. The educators worked across various educational settings, with the majority (40.3%) employed at public four-year institutions, followed by 17.7% at private four-year institutions. Others (8.4%) were employed at public two-year institutions, 5.9% worked at private two-year institutions, 10.1% were employed in public PK-12 schools, while 2.5% worked at private PK-12 schools. The remaining 15.1% reported working in other educational settings, such as “vocational institutions” and other “education colleges.” In terms of roles, the largest group of participants were assistant professors (30.3%), followed by associate professors (15.1%), and full professors (13.5%). Additionally, 11.8% of participants were adjunct instructors, 7.6% were lecturers, and 5.9% were administrators. The survey also included 8.4% of respondents who were PK-12 teachers. Nine participants (7.6%) reported other roles (e.g. instructional coaches, curriculum directors, university administrators).

Educator Familiarity of AI
In terms of familiarity with AI, a majority of educators (77.3%) reported being moderately to extremely knowledgeable about AI, while 22.7% admitted to being only slightly knowledgeable. Further, no participants reported having no knowledge of AI at all.

Use of AI at Home or in the Workforce
When educators were asked whether they used specific AI platforms at home or at work, a variety of platforms were identified. For instance, 45.7% of educators used conversational AI chatbots like ChatGPT in the workplace, while 44% reported using them at home. 68.15% utilized AI-enhanced learning management systems (LMS) such as Canvas or Moodle at work, and 15.6% used them at home. Personalized learning platforms, such as Khan Academy, were also widely used, with 23.81% of educators employing them in educational settings and a nearly equal percentage (23.02%) using these tools at home. Virtual writing assistants like QuillBot and ProWritingAid were equally popular in both educational (19.69%) and personal settings.
Language learning software was used by 33.8% of educators at work and 45.9% at home, while intelligent tutoring systems like Carnegie Learning were used less frequently at work (8.5%) and at home (6.8%). Additionally, inclusive software such as text-to-speech or read-alouds was reported being used 31.7% of the time in educational settings and 40% at home. Educators also made use of content generators like MagicSchool or Canva 42.4% of the time at work and 28.5% at home. Furthermore, AI simulations were used 34% of the time at work and 16.7% at home. AI platforms for conducting research were reported as being used 46% of the time at work and 27.4% at home. In addition, virtual voice assistants like Siri, Alexa, and Google Assistant were more frequently used at home, with 65.67% of educators using them in personal settings compared to only 16.42% in educational settings. Moreover, AI-driven quiz and assessment generators like Kahoot and Quizizz were popular in the classroom, with 53.08% of educators reporting their use in educational environments.

Student Use of AI
When educators were asked to what extent they believed students utilized AI tools, such as ChatGPT, to assist with completing assignments, the majority indicated frequent usage. Specifically, 57.1% of respondents believed that students often used AI tools between 26-75% of the time. Another 28.6% of educators believed students used AI tools sometimes, between 1-25% of the time. A smaller percentage, 9.2%, thought students almost always relied on AI (76-100% of the time), while only 5% believed students never used AI tools for assignments.

Impact on Curriculum and Teaching Practices
When educators were asked “How might you consider adjusting your curriculum in response to students using AI tools for completing assignments”, the open-ended questions revealed a range of responses. These findings were grouped into five main themes: curriculum redesign, writing assignments, critical thinking and application, ethical use and citation of AI, and no adjustments necessary. The data indicated that 25% of educators redesigned their curricula to counteract potential AI misuse. For example, many educators reported “AI-proofing” assessments by focusing on creating more specific, classroom-content-driven questions, thus minimizing the ability of AI tools to provide comprehensive answers. One respondent noted, “Lessons need to be less about recall and more about using information in new ways, meaning applying knowledge.” Another area of change involved creating authentic writing assignments, with 30% of educators indicating they had restructured their writing assignments to promote originality and deeper engagement. In fact, some of the educators noted even having introduced measures such as using platforms that allow them to track student progress. For example, one respondent shared, “I have started requiring them to write in platforms where I can see version history, such as OneDrive or Google Docs.” Educators have also adopted more personalized essay prompts and oral presentations, moving away from traditional online exams. One educator mentioned they were “moving away from online exams and requiring an oral presentation component to bigger writing assignments.” In addition to adjustments to curriculum and writing, 20% of educators focused on encouraging critical thinking and application of AI tools. Many respondents emphasized the importance of AI as a tool for brainstorming and starting projects, but not for completing them without further evaluation. As one educator noted, “AI is just a starting point. We need to then critically evaluate the responses,” highlighting the need to develop students' ability to scrutinize AI-generated content. Another respondent said they are “having students use AI to generate a response to a topic and then have students analyze bias, errors, etc.” Fifteen percent of educators emphasized the ethical use and citation of AI, with several respondents reporting the inclusion of syllabus statements that outline acceptable AI use and when it constitutes plagiarism. One respondent shared, “I have written a syllabus statement outlining acceptable and unacceptable uses of AI in writing assignments,” demonstrating the need for clear guidelines on AI’s role in academic work. Furthermore, educators are asking students to cite AI sources when used and reflect on how these tools aided their work. A participant stated, “Citing when they've used AI… writing a response about how they critically thought about the information given by AI,” showing the focus on ethical usage and reflection. However, not all educators felt the need to modify their practices. Approximately 10% of participants expressed that no adjustments were necessary. Further, some educators in subjects, like fine arts, reported that AI does not significantly affect their course content. One instructor even mentioned, “I don't believe students use AI to complete my courses, as I teach Painting and Drawing, and I do not accept digital work for my assignments.” Another educator shared that they embrace AI as long as students still create original content: “As long as AI is being used as a tool, no adjustments are needed.”

Educators’ Perception of AI on Mental Health and Mitigating Strategies
When educators were asked how they perceived AI to impact the mental health of students or educators, 95 unique responses were gathered. The most frequently mentioned concern was anxiety and stress, making up 22.92% of responses. One educator noted, "It's stressful for educators to detect plagiarism," while another highlighted, "Faculty are overwhelmed with the rapid advancement of AI." Similarly, stress among students was observed, with one educator stating, "AI increases anxiety among students, particularly around plagiarism accusations." Despite these concerns, some educators pointed out AI’s potential to alleviate stress, stating that "AI has been helpful in relieving stress for students and educators." Closely following stress, social isolation and interaction issues were a concern for 21.88% of respondents. Several educators feared the reduction of human connection, with one stating, "Students are already decreasing their interaction with others; this might make that worse." Another educator expressed concern over how AI could "lead to social isolation and decreased social skills," while others noted that AI might give students a false sense of human interaction, with comments like, "I know that interacting with AI can appear to some as though they are interacting with a human." Creativity and critical thinking issues were also a notable concern, making up 8.33% of responses. One educator remarked, "I fear that an incorrect use of AI could have effects on creativity and the acquisition of knowledge and skills," while another noted, "Students' use of GenAI can decrease their ability to develop content-based and critical thinking skills, which won't actually help them in the workplace." In terms of workload and time management, which accounted for 9.38% of responses, some educators acknowledged AI's potential benefits. One participant shared, "AI helps me finish my work in a more appropriate time frame," while another added that AI "has provided more opportunities to work on more cognitively heavy tasks."
Interestingly, 16.67% of respondents expressed no observed impact or were unfamiliar with AI's effects on mental health. As one educator put it, "I haven't noticed any effect either positively or negatively." This uncertainty was echoed by another, who said, "I'm not sure (which is why I checked a bunch of non-applicable boxes). There is so much I'm not aware of with regard to AI tools." Other themes that emerged included student dependency (5.21%), with concerns like "AI use leads to the danger of students being too dependent on it, not knowing where knowledge comes from," and issues surrounding competency and motivation (6.25%), as noted by one respondent: "AI can give students a false sense of their abilities." Smaller categories such as life balance (1.04%), media (2.08%), addiction/disruption (2.08%), and communication (1.04%) were also mentioned. For instance, one educator noted, "I think it [AI] is addictive and that addiction will always affect mental health in various ways depending on mental capacity of an individual."

Training and Resources for the Implementation of AI
When educators were asked what training programs and resources, they believed were necessary to effectively implement AI tools in the classroom, there were 87 responses, which highlighted a variety of needs. Task-saving workshops accounted for 4.6% of responses, with educators seeking training to automate tasks such as grading and assessment. One respondent shared, “We need to explore AI tools that will help us.” General training, requested by 8.0% of educators, emphasized the need for professional development covering all aspects of AI integration in education. One respondent noted, “I don't even know what to suggest, but many of us who have taught for over 20 years need basic introduction and building on basics.” Discipline specific training was highlighted by 6.9% of respondents, who asked for AI workshops tailored to their subject areas. Hands-on and practical workshops were requested by 10.3% of educators as illustrated by the following educator: “A hands-on educational workshop to demonstrate all the AI and ways that the instructor could allow the use.” Exposure to AI tools and software was requested by 11.5% of respondents, many of whom expressed a desire for ongoing workshops to stay updated on emerging technologies. One educator shared, “I need time to fully explore what is available so that I know how to make it a successful tool for student use.” Concerns around student learning and critical thinking were raised by 12.6% of educators, who emphasized the need to ensure AI enhances rather than detracts from student learning. A respondent mentioned, “New training is necessary to ensure students are not utilizing the tools as an escape from learning.” Addressing ethical AI use and bias was a major concern for 14.9% of respondents, who stressed the importance of responsible AI use in the classroom. One respondent remarked, “There needs to be a lot of discussion around bias in AI and how we adapt knowing that.” Policies and guidelines were requested by 11.5% of respondents, who sought clear rules to govern AI use in educational institutions. One educator stated, “Institutional policies and procedures as to acceptable use of AI are essential.” Also, AI detection and academic integrity accounted for 8.0% of responses, with educators expressing the need for tools like AI detectors. One respondent said, “We obviously need something to ensure that our students are actually doing their own work.” Finally, 6.9% of educators were uncertain about the specific training needed, indicating a knowledge gap in how to integrate AI effectively. One educator stated, “Honestly, I don't know what AI training would be helpful.”

Policies on AI
Regarding educator’s policies on the use of AI tools in their classrooms, the majority, about 74.4% (32 instructors), allow the use of AI tools including generative AI, but only with their approval or when they provide specific activities accompanied by guidelines on how to use these tools. Conversely, a smaller number, 14% (6 instructors), completely prohibited the use of AI tools, reflecting concerns about their impact on learning integrity or other potential issues. Only 11.6% (5 instructors) had policies that allowed for unrestricted use of AI tools, suggesting a more open attitude towards these technologies in their teaching environments. Furthermore, many participants (43%) expressed concerns about the lack of formal policies surrounding AI in their institutions and noted that clearer guidelines on AI usage, particularly in areas related to ethics, plagiarism prevention, and transparency need to be developed.

Conclusion
The findings from this study underscore the growing role of AI in education, with educators increasingly adopting AI platforms to support teaching and administrative tasks. However, concerns surrounding the ethical use of AI, its impact on mental health, and the need for professional development highlight the importance of responsible AI integration. The study’s insights will inform future curriculum adjustments, AI policies, and the development of training programs aimed at better equipping educators and students for the evolving educational landscape shaped by AI technologies. While AI holds promise for improving educational practices particularly in reducing workloads and streamlining tasks, its successful implementation requires a thoughtful and measured approach. This includes addressing its potential to undermine creativity, human connection, and independent learning. Future research should focus on developing frameworks and policies that promote responsible AI use, fostering students’ holistic development, and ensuring that AI’s benefits are maximized without compromising essential educational values.

Importance

This study is of critical importance to the field of education as it provides a comprehensive look at how AI tools are perceived by educators globally. The findings will help guide the responsible integration of AI into classrooms, ensuring that the benefits of AI such as personalized learning and efficiency are balanced with ethical considerations like bias, privacy, and mental health impacts. For the audience, this research offers timely and relevant insights into the future of AI in education and its broader societal implications.

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