Navigating AI Integration in Career and Technical Education: Diffusion Challenges, Opportunities, and Decisions

Abstract

This review paper explores the integration of artificial intelligence (AI) in career and technical education (CTE). CTE is an educational domain often overlooked in discussions about teaching and learning and notably omitted in the extant literature about AI’s application in educational settings. Although much of the existing literature focuses on AI in K-12 and higher education, CTE faces distinct challenges and opportunities in both education and the application of AI because CTE programming is more hands-on and industry-connected. This paper, grounded in Diffusion of Innovations theory, examines AI tool adoption processes among CTE educators by analyzing both barriers and opportunities. Key findings suggest that while AI offers significant benefits, its adoption is hindered by systemic factors. This paper contributes to the literature by highlighting the importance of contextualizing AI adoption within the distinct pedagogical practices and industry partnerships of CTE. It emphasizes the need for targeted strategies that address CTE-specific challenges, including robust infrastructure, equitable resource distribution, and fostering a culture of innovation among educators. The implications of this work underscore AI’s potential to bridge the gap between education and workforce demands, positioning CTE programs as critical sites for preparing students for the next phase of workforce under Industry 5.0.

1. Introduction

Artificial intelligence (AI) is an accessible, rapid, transformational system. Among its many functionalities and uses, AI can analyze and recognize data patterns, predict and forecast activities, automate and optimize, as well as make decisions and solve problems [1]. It is present and infused in many industries, including education. While AI’s integration into K-12 and higher education has garnered significant attention, its potential impact is less known about a key, growing area of education, career and technical education (CTE). The extant literature on AI in education largely omits examination of AI within CTE. Yet, given the distinct nature of CTE, which emphasizes hands-on learning, applied concepts and analysis, and industry-relevant skills [2], the adoption and application of AI in this domain present distinguishable challenges and opportunities that have been left unexplored. This review paper examines the complexities of AI integration in CTE. Specifically, by drawing on the Diffusion of Innovations theory, we interrogate the spreading of AI tools into the CTE context by analyzing current applications, barriers, and potential strategies for successful implementation.

By examining the factors influencing AI adoption among CTE educators, this paper aims to provide insights and recommendations for harnessing AI’s functional role in society, which includes supporting the CTE teaching and learning context. Developing this holistic review, this paper begins by defining AI and CTE, then it explores the literature on AI in education through the lens of Diffusion of Innovations theory. Next, this paper presents the uses, opportunities, and challenges of AI in CTE. Building off the review of the extant literature, this article offers a discussion and recommendations of diffusion analysis and decisions about AI tools within the CTE environment. Then, this paper concludes with the authors’ final reflections about navigating AI integration in career and technical education.

2. Intersections of AI and CTE

Technological Context, AI

The origins of artificial intelligence trace back to the professional fields of education. Linked to early pioneers like Alan Turing, AI developed through principles of math and logic. Consistent with its foundational basis, AI has, via machine generation, the capability to emulate human-like cognitive processes using computational and complex logic. To that end, Turing introduced the world to the Automatic Computing Engine (ACE), an innovative computer designed to store both programs and data. In 1950, Turing famously questioned, “Can machines think?”, and he explored this idea through what he termed the “imitation game” [3]. This machine process offered a method for assessing a computer’s ability to generate intelligent responses through question-and-answer format chains.

As AI developed further, John McCarthy, another influential figure in the early days of AI, argued in 1955 that the computers of his time were limited not by their capacity but by our then-current programming abilities. He is credited with coining the term “artificial intelligence” in 1956 [4]. He defined AI as the science of crafting intelligent machines.

As we moved into the 2000s, the evolution of AI in education saw a significant acceleration due to advancements in processing speeds and data capacity, transitioning from gigabytes all the way to yottabytes. This exponential increase in processing power has opened up vast new possibilities for in-depth analysis and predictive modeling in academia, making AI tools more accurate and faster, with reduced need for human intervention. Nonetheless, as Kalota [5] aptly notes, concepts of AI “allude to making machines behave with ‘intelligence’ as humans do”.

Indeed, machine learning, a branch of AI, enables machines to adapt with minimal human input. Deep learning, a more advanced subset, processes large amounts of data through intricate neural networks, simulating the way a human brain operates and learns from vast datasets. However, it is crucial to recognize that the sophisticated algorithms we see today in academic settings are not entirely new, but they have been evolving over decades [6]. Long before tools like ChatGPT became popular, AI was already a staple in educational research, used for everything from financial modeling and disease study to analyzing climate patterns and even assisting in writing college essays.

Despite the potential of AI to revolutionize academic experiences, its rise has sparked debates over academic integrity, privacy, algorithmic biases, and commercial influences, drawing mixed reactions from campus leaders [7]. These concerns underscore the complex policy and legal issues at the intersection of AI and intellectual property, highlighting the need to carefully consider how AI technologies are integrated and governed in educational settings. While AI is seemingly ubiquitous, its presence and approaches vary by industry context, even within the field of education. This article demonstrates that variation as it nests AI application within the field of career and technical education.

Career and Technical Education (CTE)

Career and technical education teachers are, in many cases, very different from teachers of core subjects [2]. First, the recognition of the coursework is different. CTE classes fall within career pathways and tend to be more applied fields. These classes relate to direct career fields such as agriculture, business, construction, engineering, hospitality, and nursing. By contrast, the traditional academic courses are even referred to as “core” or “academic” subjects, suggesting their significance to secondary education. These subjects include classes in English, math, science, fine arts, social science, and languages.

Second, the educational experiences tend to differ between CTE and core subjects. CTE courses typically prioritize hands-on learning experiences, such as labs, projects, simulations, and work-based learning (e.g., internship or apprenticeship). Core subjects tend to gravitate more to theoretical, computational, or more abstract forms of learning through lectures, discussions, readings, and formal written assignments. Interestingly though, the National Research Center for Career and Technical Education found that students in CTE programs with a strong emphasis on hands-on learning had higher academic achievement while in high school, were more likely to graduate high school, and had increased odds of enrolling in postsecondary education compared to their peers in traditional programs [8].

Third, the curricular and educational designs often have distinct origins. CTE draws from industry knowledge and skills, and CTE teachers often worked in that industry, making them more insightful for those classes. Core subjects generate their learning from established disciplinary learning and academic professional associations. Because of the CTE industry connections, the labor market places weight on industry validated approaches. Indeed, the Georgetown University Center on Education and the Workforce found that CTE graduates with industry-recognized credentials earned higher wages and were more likely to be employed than their peers without such credentials [9].

Because CTE focuses on career-based learning, AI is particularly important to this area, especially considering the automated future. While many interpret CTE as more trade learning, CTE includes many non-technical specific experiences such as communications, design analyses, risk assessments, equipment performance evaluations, and financial decision models. Thus, the learning integrates both technical and transferable knowledge, so that understanding and using current AI tools, forecasting or redesigning AI tools, contemplating and proposing new AI and unimagined uses, as well as ethical uses, are critical for the CTE environment.

3. Materials and Methods

In this review paper, we draw together recent literature reviews, corporate and government reports, and relevant studies to consider how CTE teachers can draw upon AI to enhance teaching and learning. Within this purpose, we consider—using Diffusion of Innovations theory [10]—current applications of and barriers to using AI in education, and we offer recommendations for CTE teachers and their educational organizations.

The concept of diffusion captures the processes and channels through which a new idea, tool, or practice (the innovation) is communicated to certain social groups over time [10]. An innovation can be diffused, or communicated, to certain groups through a variety of means, commonly through mass media and interpersonal and peer networks. Lack of shared backgrounds, education, experience, terminology, and needs—among other characteristics—can impede the innovation–diffusion process. Once an innovation is introduced, potential adopters go through a decision-making process that includes learning about the innovation, forming an attitude toward the innovation, deciding to adopt or reject the innovation, implementing the innovation, and seeking confirmation or reinforcement of their decision that may lead an individual to change their mind.

Social structures and norms shape the diffusion of innovations in many ways, such as the available means through which to communicate about an innovation, existing ingrained practices and approaches, hierarchies and power structures, and systemic inequities. Like individuals, decision-making authorities can adopt or reject innovations on behalf of a social system or group through mandated, collaborative, or individual-choice approaches. Although mandates can increase the speed of adoption, this approach does not ensure quality or meaningful implementation of the innovation. An innovation’s implementation has both direct and indirect consequences, some of which may be (un)desirable and (un)anticipated.

Potential adopters of an innovation desire information about how to access and use the innovation; the principles behind why and how the innovation is useful or beneficial; and its potential consequences, effects, advantages, and disadvantages within their unique context. Additionally, potential adopters consider several factors that influence adoption rates: relative advantage, compatibility, complexity, trialability, and observability. They seek to understand the relative advantage of an innovation when compared to existing ideas and practices, which can include considerations about costs, social status gains, and practicality or rationality. Potential adopters consider the compatibility of the innovation with their needs, beliefs and values, and existing ideas and practices. They also seek information about how difficult it is to use or implement the innovation (complexity), as well as the extent to which they can test out the innovation (trialability). Potential adopters also consider how easily they can see the results or effects of an innovation (observability).

Diffusion also involves uncertainty, which differentially impacts potential adopters. Innovators are individuals who introduce the innovation into a given social system. They must have access to financial resources, be able to handle high levels of uncertainty and potential failure, and understand the technical side of innovations. Early adopters are respected individuals within their organizations who serve as opinion leaders and role models for their peers, reducing uncertainty about an innovation; their (dis)approval carries great weight within the social system. Early majority adopters are still ahead of the average individual in terms of adoption speed, but are usually not opinion leaders like the early adopters; however, these individuals do contribute to the growing numbers of adopters, increasing the pressure for others to adopt. Late majority adopters are often more skeptical of the innovation and thus wait for many others to adopt first. Laggards, or the last group of individuals to adopt an innovation, rely upon the past to determine what they do in the present. Laggards are characterized as having limited access to resources, which greatly influences their willingness to adopt an innovation that might fail.

As illustrated through the description of the adopter categories, interpersonal relationships and networks are integral to the innovation–diffusion process. Whereas the early adopters of an innovation tend to rely upon mass media, subsequent adopters tend to look to opinion leaders and trusted peers who adopted before them as sources of information and models. Thus, early adopters are considered key figures for diffusion of an innovation within a given social system. Furthermore, a growing number of adopters can be a motivating factor for late majority adopters and laggards.

In the next sections, we apply Diffusion of Innovations theory [10] as a lens through which to consider themes from key reports, reviews and conceptual articles, and relevant studies about AI in education. We also consider how these broader themes apply to the context of CTE teachers. We offer recommendations for CTE teachers and their organizations to support the use and integration of AI into classrooms and curricula.

4. Diffusion Approaches–Uses and Barriers to AI in CTE Settings

4.1. AI Tools, Tests, and Uses for CTE Teachers

Recent studies, reviews of the literature, and industry reports have identified several ways in which AI might be used by educators across K-12 and higher education environments. Areas that have perhaps received the most attention are using AI to automate administrative tasks, as a teaching assistant, and to inform decision-making in teaching and learning contexts [11,12,13,14,15,16,17,18,19]. Bryant and colleagues [11] estimated that, out of the approximately 50 h per week worked by teachers, “20 to 40 percent…are spent on activities that could be automated using existing technology” ([11], p. 2). Out of their work hours, about 49% of teachers’ time went towards direct instruction and interaction with students, while professional development, administrative tasks, assessment of student work, and preparing for class filled slightly more than half of their weekly work [11]. Several studies have addressed the potential uses of AI by teachers to automate aspects of the grading process and provide more personalized and targeted feedback to students [12]. Other studies have highlighted AI uses for analyzing student data to make predictions about learners [13,14], improving teaching and learning experiences and outcomes through expert systems [13], and making decisions about course content [14].

At the same time, some authors like Grassini [12] and Chen and colleagues [15] emphasize that viewing and using AI as more of an “assistant” characterizes the early stages of AI integration in education. The authors posit that applications of AI to enhance and analyze classroom interactions and instruction will only grow, especially as AI improves over time. Others caution that AI automation will only lead to new sets of tasks and challenges [16]. Considering CTE teachers’ unique backgrounds and contexts, AI for automating tasks and serving as a “teaching assistant” might be particularly useful. CTE teachers who entered the teaching field through alternative pathways or as a second career may find that AI tools can help them streamline and improve their preparation, planning, and assessment activities.

Other applications of AI in education have focused on integration of AI into learning activities at both the individual and larger classroom levels [13,14,15,17,18,19,20,21]. For example, Wang and colleagues [17] found that 40% of the articles in their review addressed adaptive and personalized learning. Depending on the specific system’s design and intended application, such systems can tailor learners’ experiences based on learning styles and preferences and provide personalized content and feedback to learners. In terms of the larger classroom, AI applications have included tools that monitor classroom interactions and learner engagement, providing immediate feedback to instructors so that they can intervene or adjust elements of their teaching or the classroom environment to improve teaching and learning [14].

Significantly, in the U.S., the Association for Career and Technical Education (ACTE) has championed the adoption of educational technology tools, particularly in the areas of augmented reality and artificial intelligence [22]. Career and technical education programs around the U.S. have integrated the approaches to advance AI usage, skill building, and implementation to their learning environments. For instance, in 2023, Cleveland City Schools incorporated AI into the CTE engineering technology education program [23]. This initiative, which involved middle and high school CTE students, applied AI development into advanced robots from its RobotLab. The experience educated the students on coding, problem solving, critical thinking, safety, and ethical decision-making.

Knowledge transfer stands at the core of CTE. A CTE teacher at Morris County Vocational School in New Jersey reflected on her school year, in which her students co-constructed a project with the teachers by researching and planning effective professional development for staff [24]. It embedded the value of learning skills such as designing professional learning opportunities, finding and evaluating sources, and communicating findings clearly. All of these aspects also relate to AI in the CTE context.

As noted earlier, many CTE teachers may have taken non-traditional pathways into the education field; thus, these teachers may find themselves seeking additional supports and resources for identifying and providing personalized learning supports for their students. AI can provide powerful supports and resources for these teachers and their students, allowing CTE teachers to reduce some of their workload while enhancing their support for students. Further, given the subjects of their courses, CTE teachers’ classrooms likely include demonstrations of experiential, problem-, and practice-based learning as well as group work engagement. AI tools for monitoring learning progress and engagement could greatly enhance CTE instructors’ teaching by helping them better monitor students’ work on tasks and projects (e.g., welding, role playing a customer-service scenario) so that they can provide timely instructive guidance. Thus, AI tools might offer a relative advantage [10] when compared to some practices, while complementing others.

4.2. Barriers to AI Use and Integration for CTE Teachers

Although research has identified many promising applications and benefits of AI in education, several barriers exist to AI use and integration in education, including teachers’ knowledge of and preparedness to utilize AI in their classrooms [25,26,27,28,29]. Several studies have suggested that many teachers are likely to use what Rogers [10] refers to as the innovation–decision process. They are testing out technologies, largely ChatGPT, but are still unsure of its uses, capabilities, and benefits. For instance, Kaplan-Rakowski and colleagues [25] found that most participants were still in the understanding and familiarizing stages with ChatGPT, characterized by increasing their learning about how to use ChatGPT, identifying its use for specific tasks, and growing in confidence. Similarly, Mathew and Stefaniak [26] found that faculty were unsure how their students might be using ChatGPT, or how ChatGPT might contribute to academic success.

Additionally, a few studies have highlighted that the teacher participants had acquired some AI fundamentals and terminology from popular and mass media; however, these sources of information often left the teachers with knowledge gaps and misperceptions about AI [25,27,28]). For example, Lindner and colleagues [27] found that although computer science teachers were familiar with AI buzzwords, about 92% of the teachers agreed that they “lacked profound knowledge about artificial intelligence” ([27], p. 9) and about 65% pointed to the complexity of AI as a problem they face. Similarly, Velander and colleagues [28] characterized participants in their study as possessing largely superficial knowledge about AI, unable to draw linkages between concepts like privacy issues and how machine learning works and broadly struggling with the complexities of AI as non-experts. Kaplan-Rakowski and colleagues [25] found that some teachers were concerned that ChatGPT might detract from students’ development of critical thinking skills. Given their lack of knowledge and the complexity of AI, several studies and reviews pointed to the need for high-quality instructional resources that include how-to guides and best practices [20,25,26,27,28]. Further, a few studies highlighted that the educators needed clarity around policy and legal issues within AI [26,28].

Studies emphasized the fear and anxiety that educators can experience as a result of lacking AI knowledge [25,27,28]. Supporting these findings, Wang and colleagues [29] found that teachers who were the most prepared to use AI in their classrooms reported lower feelings of threat regarding AI and higher levels of innovation and satisfaction in their jobs. Although teachers’ cognitive readiness to use AI was associated with experiencing lower levels of AI threat, possessing solely knowledge about opportunities, limitations, challenges, etc., related to AI (referred to as vision) was associated with increased feelings of AI threat [29].

Against this backdrop of teacher knowledge and readiness, several scholars have emphasized potential risks of and technical issues with AI as a barrier to effective AI integration in education settings. For example, scholars have pointed to data privacy [12,30], biases [18], AI tool context dependency, and AI inefficiency [14] as challenges and barriers to AI implementation. Depending on how they were trained, AI tools may flag students with certain characteristics, such as non-native English speakers and writers [18]. The AI tools may flag these works as less proficient due to linguistic differences rather than content quality when the actual concepts or ideas are significant contributions, but the language or prose may be less than ideal. In other words, AI tools that fail to perform or deliver as expected may end up costing teachers valuable time, leading to frustration and potential abandonment of AI.

In addition to these ethical considerations, AI tools are often highly context-dependent. Their performance and reliability may vary significantly across different educational settings [14]. For instance, tools designed for urban, resource-rich CTE programs may fail in rural or underfunded schools where technological infrastructure and teacher support are limited. This disparity underscores the potential for AI to exacerbate existing inequities in educational access and outcomes. Equally important, CTE instructors or students may over-rely on the AI capacity or outputs, which could have detrimental consequences in terms of learning and practice-based effects (e.g., hazardous practices in welding, improper food preparation standards in culinary, and unsafe emergency response in paramedic clinicals). Likewise, the inefficiencies associated with poorly implemented AI systems may lead to overlooked human considerations, diminished trust, and possible abandonment for AI applications that may have significant benefits and accuracy.

Considering CTE teachers’ adoption of technology, Kotrlik and Redmann [31] found that 92% of CTE teachers were self-taught in terms of their knowledge about technology; 90% identified workshops and conferences as important sources of learning, as well as from their colleagues (81%). In addition to echoing themes identified within the AI in the education literature, CTE educators reported facing moderate barriers related to availability of time to learn about and integrate technology into their courses and availability of technology and technical support [31]. Turning to diffusion of innovations, the barriers identified within the literature related to the kinds of information CTE teachers would likely seek to make a decision about AI adoption. Studies suggested that although teachers were aware of AI, many were still developing their understanding of the technology and systems, but they struggled to find appropriate resources to help them fully address knowledge gaps with such an unfamiliar and complex topic. Without trusted opinion leaders and near peers to follow as models [10], many CTE teachers will delay adoption of AI. Further, lacking time and appropriate resources might reasonably complicate potential adopters’ ability and willingness to try out (trialability [10]) and see (observability [10]) the value of AI.

5. Discussion and Recommendations

As demonstrated at the beginning of this article, career and technical education (CTE) operates in a significantly different manner than traditional education offerings in core or academic subjects. Conceptualizing an analysis of the contributions from AI systems to the CTE setting, we drew on the Diffusion of Innovations theory [10] to process our findings from a meta-narrative approach. This analysis of the literature suggests three distinct contributions to reconceptualize how educational scholars may explore how CTE teachers draw upon AI to enhance their teaching and learning. Namely, the collective literature suggests that the Diffusion of Innovations theory about how CTE teachers use AI for teaching and learning offers three categories of research lessons –opportunities and benefits of AI, systematic integration challenges associated with AI, and decision-making and work parameters related to AI usage.

5.1. Opportunities and Benefits

Drawing on the Diffusion of Innovations theory [10], we presented an analysis of AI tools, tests, and uses for CTE teachers revealing an optimistic perspective about the opportunities and benefits. Based on the extant literature, the perceived relative advantage of AI, particularly in automating administrative tasks, personalizing learning experiences, and facilitating data-driven decision-making, connects with the theory’s emphasis on the innovation’s perceived superiority over existing practices. CTE teachers recognize the potential that AI offers to enhance their instruction and alleviate administrative burdens that they must often address as career specialties in their uniquely represented fields. Given these valued contributions, AI affords CTE teachers a positive perception about their instruction tasks, which remain the critical responsibility of their position.

Adding to the value proposition, the diffusion of innovation reinforces AI compatibility with CTE curricula and teaching methods. As highlighted in the extant literature, CTE teachers’ interest in seeing AI, as critical agents to achieve their work, supports the theory that innovations are more readily adopted when they align with established practices, such as instructional responsibilities [10]. Significantly, this alignment between a technology tool or system and the primary work demonstrates compatibility, which enable CTE teachers to appreciate the complexity reduction with integrating AI. In other words, the instructional technology compatibility demonstrates AI’s highly accessible and less intimidating features for CTE teachers. Topping it off, the emphasis on user-friendly interfaces and intuitive design reinforces this compatibility and AI’s value, and those considerations also offer a smoother adoption process of AI systems.

A solution that has been offered, yet not evaluated, is a comprehensive offering of accessible, high-quality professional development programs [32]. These professional development programs should be targeted to practicing teachers, who are actively engaged with students, and student teachers, who are learning the craft of the profession. The focus of such professional development offerings is to bridge the knowledge gaps highlighted in studies and observable practices of teachers and students in teacher education programs.

While professional development tends to present how-to guides, best practices, and contextualized examples of AI applications in specific CTE disciplines, such programs should also tie in learning science components such as cognitive elaboration and cognitive load theory [33]. Application of cognitive load theory, for instance, helps CTE teachers temper the amount of information and reduce extraneous cognitive load. Common practices include effective learning science concepts such as scaffolding and interleaving, which break down concepts into manageable chunks and progressively add complexity, as well as the role of spacing, which incorporates a distributed practice to learning with smaller loads of content spaced over time to help with developmental learning and memory retrieval. In addition, professional development tends to focus on fewer but tested and well-understood tools, so the CTE teachers and students in teacher education programs may develop deeper knowledge with a concentrated set of tools. This approach ensures sufficient practice and knowledge development before deploying the AI system to the students in the CTE classes.

5.2. Systematic Integration Challenges

The systematic integration of AI into regular CTE curricula presents its own challenges. These integration challenges are primarily rooted in the existing traditions of the CTE field, educational infrastructure, and the readiness of schools and administrators to embrace new technologies. Despite the potential benefits, the research has previously observed notable lags in integrating AI across education, including within CTE. Relative to the core subjects, CTE programs already are outcasts in the educational environment; therefore, integration that emerges from CTE teachers’ requests, as opposed to the core or academic subjects, often leads to a presumptive pause questioning a technology tool’s need, especially tools such as AI. Along those same lines, CTE integration of AI raises skepticism and caution including adoption hesitancy or delays due to insufficient institutional support, lack of funding, and the slow pace of curricular reforms. CTE instructors might attribute this inertia to the complex decision-making processes within school buildings and district offices. Nonetheless, while decision-making processes in the educational system may be slow and cumbersome, often involving multiple stakeholders with varying interests and priorities, these administrative burdens are not the only obstacles behind the filtered or process-clotting barriers behind the innovation diffusion, as this research has illuminated [34].

To create a more systematic adoption of AI, this research suggests that educational policymakers and leaders should recognize AI’s potential to enhance career and technical education. The CTE field should also develop strategic plans that include engaging professional development attracting greater interest in AI implementation into classroom learning, updated curricular guidelines infusing AI, and ongoing support for teachers and students so that innovation and risks are not feared [20]. This research suggests that much of the resistance may also stem from a culture shift. Mindsets and practices that shape the culture could mitigate resistance and facilitate the smoother integration of AI tools into CTE programs. By doing so, CTE instructors might be more willing to integrate new approaches and AI systems as part of the regular teaching improvement process steps—folded into professional practice [12].

The reframed culture shift tends to normalize innovation, value professional collaboration, and could reframe AI as a tool to augment or improve educational practices rather than replace human expertise—consistent with the Industry 5.0 focus. This infusion of industry application has been long an identified gap within CTE [35]. Also, previous research has repeatedly highlighted the critical element of organizational readiness in educational reform [31]. This mindset allows for educators to feel empowered and supported, so they are able to exercise their agency.

This organizational culture shift might begin by establishing environments where experimentation with AI is encouraged and failure is embraced as part of the learning process. For instance, a culinary instructor could use AI recipe generators as a creative classroom exercise. Such an activity would encourage students to critique and refine the AI’s output. This approach aligns with findings from [20], which emphasize the need for innovation to be embedded in regular teaching practices to reduce fear and resistance.

With any change, systemic support from school administrators and policymakers helps advance the change in a welcoming setting. Educational change researchers have demonstrated that priorities of reducing bureaucratic hurdles require integrating CTE teachers in the process, and such actions would likely be no different when considering AI tools in the CTE curriculum. In short, these solution-centered measures align with Rogers’ Diffusion of Innovations theory, which underscores the importance of reducing barriers to trialability and observability to facilitate adoption [10].

5.3. Decision-Making and Work Parameters

Within the context of CTE, several factors mediate the decision-making frameworks that guide the use of AI. When processing the Diffusion of Innovations theory [10], particularly the perceived attributes of the innovation, the relative advantage associated with the use of the innovation is weighed. In this instance, the inquiry involves asking what are the learning objectives, industry standards, and the educators’ acceptance of AI application in the school or district? These questions are even more complex for CTE teachers, as they often face the dual challenge of aligning their instruction with industry practices while also ensuring that it meets educational standards. AI tools offer a different experience: they serve both as a potential educational technology to enhance learning and as a potential instrument that illustrates an industry function or technology feature. However, whether one decides to integrate AI tools into teaching practices is a much deeper inquiry. CTE teachers must wrestle with questions such as: Is the AI tool appropriate for a specific learning outcome?; Is the AI tool useful for industry application?; Are the students ready to engage with these AI tools?; What technology supports or infrastructure must be established prior to implementation [18]?

Furthermore, broader educational policies and the support systems shape the work parameters within which CTE teachers operate with the AI tools at their institutions. The effectiveness of AI integration is contingent upon a supportive infrastructure that includes access to reliable technology, ongoing professional development, and a flexible curriculum to incorporate AI-driven innovations. Therefore, decision-making about AI in CTE is not just about individual teacher preferences, but the decision-making also includes consideration of institutional policies and the collective commitment to leveraging technology to enhance vocational education. This school-based decision-making emphasizes the need for a coordinated approach encompassing policy support, infrastructure development, and teacher empowerment to fully realize the benefits of AI in CTE settings [13].

6. Conclusions

In conclusion, while barriers to AI integration in CTE will likely persist, the overall impact of AI diffusion is also likely to be positive. That is, the odds are high that AI technologies will be viewed as largely beneficial with a net positive effect. The decision to integrate AI tools into teaching practices, in a manner of technological diffusion, still involves careful consideration of various factors including learning objectives, industry standards, and the readiness of both educators and students. As AI continues to evolve, new challenges and opportunities will follow, and CTE educators will have the opportunity to embrace AI’s potential of enhancing student learning, streamlining administrative tasks, and bridging the gap between education and industry.

Indeed, the diffusion of AI integration into career and technical education reflects a pivotal opportunity to enhance educational outcomes and respond to emerging workforce needs. Nonetheless, much of this examination remains underexplored. Notably, the CTE field needs future research exploring the effectiveness of AI-driven simulations in fostering real-world skill acquisition [36]. Unlike traditional learning methods and the extant approaches to simulations, advanced AI simulations now have the capacity to create immersive, risk-free environments. These educational simulations permit CTE students to engage in and practice complex tasks (e.g., construction, healthcare, and information technology) with real-time feedback, and scaffold learning such as building off the learning blocks to gain greater depth in a subject area. Already, AI-generated virtual reality platforms in healthcare programs have been shown to improve precision and reduce errors among students practicing surgical techniques, which may be applied to other settings such as CTE [37].

Another area for future research includes examining how industry partnerships can further align AI usage and creation within CTE. While industry explores specific applications of AI, its workforce should also be prepared in the educational setting. Nonetheless, the research is less developed on learning alignment between CTE classrooms and industry expectations, especially in high-demand fields, which require a quicker turn-around for workforce readiness. For example, future research should inquire about how collaborations with industries should be designed with the rapidly changing availability and functions of AI tools. In addition, it may explore how CTE addresses the evolving skill gaps, as technology changes quickly, particularly in sectors such as advanced manufacturing, renewable energy, or IT, which often demand a future-ready workforce with a narrower time from need to workforce availability.

Finally, another critical area of research involves exploring how AI can promote equity and accessibility in CTE programs while also addressing broader industry needs and societal trends. The future of AI and work, tied to Industry 5.0 priorities, emphasizes human–AI collaboration to enhance productivity and creativity. Accordingly, future research must examine how CTE programs integrate these contextual environments. For instance, future research should ask: How are CTE program partnerships with Industry 5.0-focused companies restructuring learning and workforce readiness?; What efforts are CTE programs making to advance sustainable practices and ethical labor principles while also addressing global challenges?; How are laws and policies hindering or advancing these efforts?; What are AI impacts on long-term career outcomes for CTE students?

In short, by embracing AI as a valuable tool and adapting to the changing landscape of technology, CTE professionals can improve teaching, adopt early innovation, model manageable risk-taking, and participate in the increasingly automated world—which is no doubt shaping the field of CTE.