Integrating AI Into Graduate I-O Training: Development and Implementation of a Competency-Based Curriculum Model

Acknowledgements: Correspondence concerning this article should be addressed to Peter “Pete” McLemore, Department of Psychology, The University of Texas at Arlington, pbm3106@mavs.uta.edu. This work was supported by a Teaching Innovation Grant from The University of Texas at Arlington College of Science.

Introduction

Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, is rapidly transforming the practice of industrial-organizational (I-O) psychology. These tools are already being used across core I-O functions, including job analysis, applicant screening, and adaptive training (Aguinis et al., 2024). At the same time, concerns surrounding bias, ethical use, and overreliance remain prominent (Carmichael, 2024; Chen, 2023). Scholars forecast that AI will fundamentally reshape I-O practice within the next five years (Gutierrez & Landers, 2024; Weiner et al., 2024), creating an urgent need to reconsider how graduate training prepares students for this evolving landscape.

In response to this shift, the present paper describes a case study of how one university integrated AI competencies into their I-O psychology graduate curriculum. The goal of this paper is twofold: (a) to provide a practical, competency-based framework that other programs can adapt when incorporating AI into their training, and (b) to stimulate dialogue within the field regarding effective approaches to AI integration in I-O education. Rather than offering a prescriptive “one-size-fits-all” solution, this case illustrates a structured, iterative approach to curriculum transformation that may serve as a template or starting point for other programs as they prepare students for impactful careers.

Traditional I-O competencies (e.g., data literacy, ethical reasoning, and domain expertise) remain essential. However, they must now be applied within AI-augmented contexts. For example, SIOP (2023) emphasized that AI-based assessments must meet the same standards of validity, reliability, fairness, and transparency as traditional methods, despite differences in how those standards are evaluated. This reinforces the need for integrated competency development rather than treating AI as an isolated technical skill.

Theoretical and Educational Rationale for AI Integration

Emerging research suggests that integrating AI into higher education can enhance student engagement, critical thinking, and preparedness for AI-augmented work environments. For example, Walter (2024) highlighted the importance of prompt engineering, AI literacy, and critical thinking as foundational skills, with students reporting increased confidence in applying AI tools following exposure.

However, these benefits are not without caveats. Lin and Chen (2024) demonstrated that AI-integrated learning environments can both enhance creativity and engagement (via interactivity, personalization, idea generation) while also constraining creativity and motivation (via rigid structures, disengagement, and performance anxiety). This duality underscores a key implication: AI integration must be intentional and structured to maximize benefits while mitigating risks.

Additionally, AI is already reshaping instructional practices. Educators are using tools such as ChatGPT to adapt curricula, revise lesson content, and personalize learning experiences (Karataş et al., 2025). This suggests that AI is not only a content domain but also a pedagogical tool, further reinforcing the need for competency-based integration.

 Project Overview and AI Competency Model

To address the growing need for structured AI training in I-O psychology, a university-supported initiative was developed to design and implement an AI competency model for graduate education in I-O psychology at the University of Texas at Arlington. This project was guided by an essential research question: “What are the core competencies that I-O graduate students need to develop in school to use AI successfully in the field?” The project followed a three-phase approach consisting of needs assessment, curriculum transformation, and evaluation, with each phase building toward the integration of AI competencies into existing coursework.

Phase 1: Needs Assessment

The first phase, conducted in spring 2025, focused on identifying the AI-related competencies needed for effective practice in I-O psychology. Consistent with established training design principles, a needs assessment serves as a critical first step to ensure that training is aligned with actual job demands and organizational requirements (Goldstein & Ford, 2002). To ensure the model reflected real-world application, we used a multimethod approach that combined existing research, job analysis data, and input from practicing professionals.

We began by reviewing recent literature on artificial intelligence in organizational contexts, with particular attention to how generative AI is being used in applied settings and what skills are required for effective use. Scholarly databases (including the university library resources and Google Scholar) were used to identify research on generative AI applications in I-O-related fields, competency requirements for AI-integrated work, and broader discussions of ethical and practical implications (e.g., Aguinis et al., 2024; Archidivilli et al., 2024; Ekuma, 2024; Graßmann & Schermuly, 2021). This review was complemented by examining existing competency frameworks (e.g., AI in Government Act of 2020) to identify broadly relevant AI capabilities. Together, these sources helped establish an initial foundation and informed the selection of I-O–relevant roles for further analysis using O*NET (National Center for O*NET Development, 2026).

To ground the model in actual job demands, we reviewed I-O–related roles within O*NET and extracted representative job tasks across domains such as training, talent management, consulting, and analytics. Associated job tasks were extracted and consolidated into a comprehensive list of distinct activities representative of applied I-O work, particularly those aligned with master’s-level training and early-career practitioner roles. This step ensured that competency development was grounded in actual job demands rather than abstract skill definitions. Given this applied focus, the resulting competency model is most directly applicable to practice-oriented graduate programs. Programs focused on training academics and researchers (e.g., PhD programs) may require additional competencies not fully captured in the current model.

We then conducted semistructured interviews with I-O practitioners to better understand how AI is currently being used and what capabilities are most critical for success. The interview protocol was developed iteratively and focused on areas such as current AI use, prompt development, and workflow integration. For example, participants were asked, “Could you describe how you currently incorporate AI tools into your daily work?” and “How do you develop prompts or queries to obtain useful, context-specific results?”

A total of 12 subject matter experts (SMEs) representing a range of applied roles (e.g., leadership development, training, analytics) participated in these interviews. The majority were alumni of the program (n = 11), with one additional participant recruited via referral. Participants primarily held master’s degrees in I-O psychology (n = 10), with one holding PhD and one completing a PhD while working. SMEs reported between 1 and 10 years of professional experience in the field. Their responses were systematically coded by members of the research team to identify recurring themes and competency domains, with discrepancies resolved through discussion and consensus. Initial coding drew from existing frameworks, but additional competencies emerged directly from the data, reflecting how practitioners are adapting to AI in real time.

This process resulted in an initial set of 29 competencies, which were then consolidated into 12 higher order competencies based on conceptual overlap and practical relevance. Notably, the resulting competencies extended beyond technical skills to include areas such as critical evaluation, adaptability, collaboration, and ethical judgment; highlighting the importance of human-centered capabilities in AI-supported work.

Following competency consolidation, detailed descriptions and illustrative behaviors were developed for each higher order competency. This resulted in 58 illustrative behaviors that reflect how these competencies are enacted in practice (e.g., refining AI-generated outputs, selecting appropriate tools, and ensuring ethical data use). These behaviors were designed to support both instruction and assessment by translating abstract competencies into observable actions.

Finally, a survey was administered to SMEs to assess two dimensions: instructional priority, defined as how important is it that a given task be taught or developed during a graduate I-O psychology program, and the proficiency at entry, defined as the level of proficiency a graduate should possess upon entering the workforce to perform this task effectively. Together, these dimensions capture both the importance of a task for practice and the extent to which graduates are expected to already demonstrate competence, allowing for the identification of potential training gaps.

Results indicated that instructional priority consistently exceeded proficiency at entry (average difference = 0.47), suggesting meaningful skill gaps that graduate training should address. A weighted scoring approach was used to identify the most critical competencies, resulting in a final set of eight competencies and 20 key behaviors that informed subsequent curriculum integration (see Table 1).

 

Table 1

AI Competency Model for I-O Practice

Competency	Definition	Illustrative behaviors
Change leadership & advocacy	Advocating for the responsible adoption of AI by demonstrating its connection with organizational priorities, emphasizing the benefits, and reinforcing the necessity of human judgment in AI-supported practices.	·       Communicate the value of AI tools by aligning them with organizational goals and motivating teams to embrace new solutions. ·       *Highlight unique human capabilities—like critical thinking and contextual judgment—to complement AI use and reinforce professional value.
Content generation & refinement	Using AI to generate, refine, iterate, and translate content of various formats for organizational and work-related goals.	·       Convert complex ideas or frameworks (e.g., road maps, leadership models) into concise summaries using AI, then integrate into presentation slides. ·       Leverage AI writing tools to polish existing content—scripts, job descriptions, or lesson plans—while retaining human tone and intent. ·       Maintain quality by reviewing AI-generated drafts for style, grammar, and content consistency before finalizing deliverables.
Critical evaluation & fact checking	Validating accuracy, detecting bias, and correcting AI-generated outputs through source verification, iterative refinement, and thoughtful analysis before workplace application.	·       *Identify and correct inaccuracies in AI-generated outputs by comparing them against independently verified data or trusted external sources. ·       Review AI-generated proposals or content and iteratively adjust prompts until results meet organizational standards for tone, structure, and relevance. ·       *Conduct manual quality checks—including data triangulation or parallel analysis—before distributing or acting on AI-assisted outputs. ·       Use chain-of-thought tools or model reasoning views to diagnose misinterpretations in AI responses and revise inputs accordingly. ·       Design assessments or prompts that require candidates or users to explain their reasoning behind AI-generated responses, ensuring true understanding and not blind acceptance.
Curiosity & adaptive learning	Proactively exploring emerging AI solutions, unlearning outdated habits, and updating practices to keep pace with technological advancements.	·       Seek out and experiment with new AI tools or features, adjusting workflows as technologies evolve.
Data privacy & governance	Safeguarding sensitive information and upholding ethical standards to ensure fair, secure, and compliant AI use.	·       Screen inputs for confidential or personally identifiable information (PII) and redact sensitive fields before submission to AI tools. ·       Audit AI outputs for potential bias or risk and intervene when automated suggestions may lead to unfair or noncompliant outcomes.
Human- centered judgement	Exercising discernment to balance AI automation with human oversight, ensuring decisions and communications preserve ethics, empathy, and contextual understanding.	·       Distinguish between tasks that benefit from AI automation and those that require human oversight, reserving complex or sensitive work for manual handling. ·       Use AI to generate first drafts or options, then conduct thorough human review to ensure quality, contextual accuracy, and ethical alignment. ·       *Maintain a human-in-the-loop policy for decision-making, using AI outputs as advisory input while preserving final judgment for people leaders. ·       Preserve authenticity and human connection in communications by personalizing AI-generated content, especially in sensitive situations. ·       Advocate for human expertise by highlighting intuition, ethical reasoning, and contextual awareness in conversations about AI adoption.
Knowledge-based strategic integration	Applying specialized disciplinary knowledge to shape how AI is used, ensuring that its contributions are both analytically sound and meaningfully connected to an organization’s long-term priorities.	·       Clarify the purpose and intended outcome of AI-assisted tasks to ensure outputs contribute to broader organizational goals. ·       Synthesize domain-specific research (e.g., IO psychology, organizational behavior) alongside AI outputs to ensure accurate, evidence-based insights.

Note: *Special consideration illustrative behavior (highest rated behaviors)

 

Collectively, this needs assessment produced a competency model grounded in practitioner input and real-world job demands. Importantly, the model emphasizes not only technical AI skills but also the broader capabilities required for effective human–AI collaboration, including critical evaluation, ethical reasoning, and strategic integration. These findings served as the foundation for embedding AI competencies into the graduate training program at UTA.

 

Phase 2: Curriculum Transformation

 

The second phase, completed in summer 2025, focused on translating the competency model into actionable instructional practices. Rather than introducing standalone AI courses, faculty integrated competencies directly into existing graduate courses by revising syllabi and embedding targeted assignments aligned with specific skills. This approach was designed to make AI use context-specific and immediately relevant to core I-O tasks.

 

Instructional activities emphasized applied use of AI in ways that mirror professional practice, including evaluating AI-generated outputs, refining AI-assisted work products, and integrating AI tools into common I-O functions such as data analysis, training development, and assessment design. To support implementation, faculty were provided with both general and course-specific assignment templates that could be adapted to different content areas.

 

General strategies that can be applied across courses were provided to faculty (see Table 2). These activities focused on developing foundational competencies such as critical evaluation, human-centered judgment, and adaptive learning by engaging students in tasks like critiquing AI outputs, refining prompts, and exploring new tools. Examples of how these strategies can be embedded within specific I-O courses were also provided (see Table 3), illustrating how AI integration can be tailored to domain-specific learning objectives (e.g., psychometrics, organizational behavior, and training).

 

Table 2

General Suggestions for Curriculum Transformation

Strategy	Description	Competencies mapped
Case-based simulations	Present students with real-world HR/IO scenarios that involve flawed or incomplete AI outputs. Ask students to critique, refine, and validate results.	·       Critical evaluation & fact checking ·       Human-centered judgment
Prompt refinement challenges	Give students vague or poorly structured prompts and ask them to revise them for clarity, context, and task fit.	·       Prompt engineering ·       Content generation & refinement
AI-augmented assignments	Have students draft something using AI (e.g., job descriptions, training outlines) and then revise it with human input, emphasizing tone, ethics, and clarity.	·       Content generation & refinement·       Human-centered judgment
Tool exploration logs	Ask students to experiment with a new AI tool or feature each week and reflect on its usefulness, limits, and ethical risks.	·       Curiosity & adaptive learning ·       Data privacy & governance
Team presentations on AI use cases	Assign groups to research and present emerging uses of AI in HR or OB, highlighting potential benefits, risks, and organizational strategy implications.	·       Change leadership & advocacy·       Strategic foresight

 

Table 3

Course-Specific Suggestions for Curriculum Transformation

Strategy	Description	Competencies mapped
AI tool validation lab (psychometrics)	Students use AI to generate assessment items, then validate them using psychometric methods (e.g., item difficulty, discrimination).	·       Content generation & refinement·       Critical evaluation & fact checking
Leadership & advocacy memo (OB)	Students draft a change management plan using AI to advocate for ethical AI adoption in their organization.	·       Change leadership & advocacy·       Content generation & refinement
Training script generation (employee training)	Students prompt AI to generate outlines for a training session, then revise using best practices from instructional design.	·       Content generation & refinement·       Prompt engineering
Onboarding content redesign (business psychology)	Students use AI to draft onboarding documents, then personalize content for tone, clarity, and engagement.	·       Content generation & refinement·       Human-centered judgment

 

Phase 3: Implementation and Evaluation

The final phase, conducted from fall 2025, involved implementing AI-integrated instructional activities and evaluating their effectiveness. AI-based assignments and instructional strategies were deployed across multiple graduate courses, with implementation occurring in two waves to allow for iterative refinement based on early experiences. Evaluation efforts included student and faculty surveys assessing perceived competency development, reactions to AI-integrated instruction, and knowledge assessments. These measures were designed to capture both subjective experiences (e.g., confidence, perceived usefulness) and indicators of learning related to AI competencies. This phase emphasized not only the deployment of AI-integrated curriculum but also the systematic collection of feedback to inform ongoing refinement of instructional approaches.

Preliminary Findings and Insights 

Five out of 15 graduate students completed the end-of-semester survey. The sample was primarily female (80%) and represented both master’s (60%) and doctoral (40%) I-O psychology students. The average age of participants was 26 years. Students were enrolled across multiple courses that incorporated AI-related content, including Organizational Behavior, Employee Training, and an I-O Psychology Internship course. These results should be interpreted cautiously given the small sample size but provide initial insight into student experiences with AI-integrated instruction.

Student Survey

We first examined students’ perceived AI self-efficacy following exposure to AI-integrated coursework. Overall, students reported relatively high levels of confidence in their ability to use AI effectively (M = 4.08, SD = .69), using a 5-point scale where anchors ranged from 1 = strongly disagree to 5 = strongly agree. In particular, students felt most confident in their ability to combine AI outputs with their own expertise to make informed decisions and to use AI responsibly in professional settings. Students also reported strong capability in identifying when AI could improve their work processes. Comparatively lower, though still positive, ratings were observed for adapting to new AI tools and using AI efficiently for task completion, suggesting some variability in applied fluency despite generally high confidence. See Table 4 for results.

 

Table 4

AI Self-Efficacy Results

AI self-efficacy	M	SD
I am confident in my ability to use AI tools to complete work-related tasks efficiently.	3.60	.80
I feel capable of identifying when AI can improve my work processes.	4.20	.40
I can adapt to new AI tools or features without extensive instruction.	3.80	.75
I know how to combine AI outputs with my own expertise to make informed decisions.	4.40	.49
I feel prepared to use AI responsibly in a professional setting.	4.40	.49
AI self-efficacy composite	4.08	.69

 

We next examined competency development using two complementary indicators: competency, reflecting students’ current ability to perform AI-related behaviors, and mastery, reflecting the extent to which those abilities improved as a result of AI-integrated coursework (see Table 5). Across competencies, students reported relatively high levels of perceived ability, with composite scores generally above the midpoint of the scale. The highest competency ratings were observed for content generation and refinement (M = 4.27), followed by change leadership (M = 4.10) and critical evaluation and fact checking (M = 4.08), suggesting that students felt most capable in using AI in applied, task-oriented competencies to produce and refine work outputs, communicate its value, and evaluate AI-generated information.

In contrast, lower competency ratings were observed for human-centered judgment (M = 3.44) and knowledge-based strategic integration (M = 3.30), indicating that more complex, integrative skills involving contextual decision-making and domain-specific application of AI may be less developed. Notably, these competencies also showed higher variability, suggesting inconsistent experiences or understanding across students.

Patterns for perceived mastery differed somewhat from competency levels. The greatest reported improvements were observed in Data Privacy and Governance (M = 3.80), indicating that students perceived substantial growth in their ability to manage sensitive information and evaluate risks associated with AI use. Moderate gains were observed in critical evaluation and fact checking (M = 3.00) and human-centered judgment (M = 2.96), suggesting that coursework may have supported development in evaluative and oversight-related skills.

Alternatively, lower mastery ratings were observed for curiosity and adaptive learning (M = 1.80) and knowledge-based strategic integration (M = 2.40), indicating more limited perceived growth in developing habits of ongoing AI exploration and in integrating AI with domain-specific expertise.

Table 5

Student’s Self-Report Competency and Mastery Ratings

Competency	Illustrative behavior	Competency rating		Mastery  rating
		M	SD	M	SD
Change leadership (CL)	Communicate the value of AI tools by aligning them with organizational goals and motivating teams to embrace new solutions.	4.00	.63	2.80	.98
	Highlight unique human capabilities—like critical thinking and contextual judgment—to complement AI use and reinforce professional value.	4.20	.75	3.00	1.10
	CL composite	4.10	.70	2.90	1.04
Content generation & refinement (CGR)	Convert complex ideas or frameworks (e.g., road maps, leadership models) into concise summaries using AI, then integrate into presentation slides.	4.20	.75	2.40	1.02
	Leverage AI writing tools to polish existing content—scripts, job descriptions, or lesson plans—while retaining human tone and intent.	4.20	.40	2.80	.75
	Maintain quality by reviewing AI-generated drafts for style, grammar, and content consistency before finalizing deliverables.	4.40	.49	3.20	.75
	CGR composite	4.27	.57	2.80	.91
Critical evaluation & fact checking  (CEFC)	Identify and correct inaccuracies in AI-generated outputs by comparing them against independently verified data or trusted external sources.	4.20	.75	3.20	.75
	Review AI-generated proposals or content and iteratively adjust prompts until results meet organizational standards for tone, structure, and relevance.	4.00	.63	2.40	.80
	Conduct manual quality checks—including data triangulation or parallel analysis—before distributing or acting on AI-assisted outputs.	4.20	.75	3.20	.75
	Use chain-of-thought tools or model reasoning views to diagnose misinterpretations in AI responses and revise inputs accordingly.	4.00	.63	3.20	.75
	Design assessments or prompts that require candidates or users to explain their reasoning behind AI-generated responses, ensuring true understanding and not blind acceptance.	4.00	.63	3.00	1.10
	CEFC composite	4.08	.69	3.00	.89
Curiosity & adaptive learning (CAL)	Seek out and experiment with new AI tools or features, adjusting workflows as technologies evolve.	3.60	.80	1.80	.98
	CAL composite	3.60	.80	1.80	.98
Data privacy & governance (DPG)	Screen inputs for confidential or personally identifiable information (PII) and redact sensitive fields before submission to AI tools.	3.60	1.50	3.80	.75
	Audit AI outputs for potential bias or risk and intervene when automated suggestions may lead to unfair or noncompliant outcomes.	3.60	1.50	3.80	.75
	DPG composite	3.60	1.50	3.80	.75
Human-centered judgement (HCJ)	Distinguish between tasks that benefit from AI automation and those that require human oversight, reserving complex or sensitive work for manual handling.	3.60	1.50	3.20	1.33
	Use AI to generate first drafts or options, then conduct thorough human review to ensure quality, contextual accuracy, and ethical alignment.	3.40	1.02	3.00	.63
	Maintain a human-in-the-loop policy for decision-making, using AI outputs as advisory input while preserving final judgment for people leaders.	3.40	1.36	3.00	1.10
	Preserve authenticity and human connection in communications by personalizing AI-generated content, especially in sensitive situations.	3.20	1.33	2.60	1.02
	Advocate for human expertise by highlighting intuition, ethical reasoning, and contextual awareness in conversations about AI adoption.	3.60	1.50	3.00	1.10
	HCJ composite	3.44	1.36	2.96	1.08
Knowledge-Based Strategic Integration (KBSI)	Clarify the purpose and intended outcome of AI-assisted tasks to ensure outputs contribute to broader organizational goals.	3.40	1.36	2.40	1.36
	Synthesize domain-specific research (e.g., I-O psychology, organizational behavior) alongside AI outputs to ensure accurate, evidence-based insights.	3.20	1.33	2.40	1.02
	KBSI composite	3.30	1.35	2.40	1.20

 

Taken together, these results suggest that AI-integrated coursework may be particularly effective in supporting applied, task-oriented competencies (e.g., content generation, evaluation, and responsible use), while more complex competencies involving strategic integration, adaptability, and human-centered judgment may require more sustained instructional emphasis. 

Student Course Feedback

Students also provided feedback on their courses that integrated AI into the coursework. Student feedback across courses was generally positive, with participants reporting that AI integration supported learning, increased confidence, and was relevant to course objectives (see Table 6). In both the I-O Internship and Organizational Behavior courses, students reported relatively strong agreement that AI improved their understanding of course material (M = 4.00–4.33) and that AI-related activities aligned with course goals (M = 4.33–4.50). Students in these courses also indicated increased confidence in using AI in academic or professional settings and expressed strong support for including AI skill-building activities in future coursework. However, responses from the employee training course were more mixed. Although students still reported that AI supported their understanding (M = 4.00), lower ratings were observed for perceived relevance, confidence gains, and support for future inclusion. 

Table 6

Student Feedback on Course-Specific AI Integration

	I-O internship		Organizational behavior		Employee training
Questions	M	SD	M	SD	M	SD
The use of AI in [Field-1] improved my understanding of the subject matter.	4.00	1.00	4.33	.47	4.00	1.00
The AI-related assignments or activities felt relevant to the course goals.	4.50	.50	4.33	.47	3.50	.50
I feel more confident using AI tools in academic or professional settings because of [Field-1].	4.50	.50	4.00	.82	3.00	.00
I would support the inclusion of AI skill-building activities in future psychology or I-O courses.	4.50	.50	4.33	.47	3.50	.50

Note: “[Field-1]” auto-populated with the respondent’s corresponding course title in the survey software.

Qualitative responses from students provided additional context and revealed three consistent themes across courses (see Table 7). In the internship course, AI was primarily used as a targeted, self-directed tool for career development, with one student noting it helped “improve my confidence during interviews” and helped with summarizing job materials, reflecting practical but unstructured use. In organizational behavior, students emphasized a shift toward greater acceptance of AI, noting that tools were “not viewed as negatively” as in prior experiences and that integration into coursework were viewed positively, highlighting the role of faculty framing in shaping attitudes. In contrast, feedback from Employee Training reflected that although AI could be utilized, it was not formally and explicitly integrated into assignments, echoing the need for more structured guidance for integration. Taken together, these patterns suggest that students perceive the greatest value when AI is intentionally embedded into coursework, whereas passive or optional use may limit perceived relevance and skill development.

Across student responses, a key takeaway emerged: Structured, competency-based integration is critical for maximizing engagement and skill development, whereas permissive or ad hoc use may limit the perceived value of AI in coursework.

Table 7

Qualitative Themes by Course

Course	Theme	Description	Example quote
I-O internship	Targeted, self-directed use	AI used independently for practical career tasks (e.g., interview prep, job materials); limited structured integration	“AI has helped me come up with prompts on how to improve my confidence during interviews and summarize key points…”
Organizational behavior	Normative acceptance and encouragement	Faculty openness fostered positive attitudes toward AI; integration viewed as forward-looking even without standout activities	“AI tools aren’t viewed as negatively… pretty cool the program is trying to integrate AI…”
Employee training	Permitted but minimally integrated use	AI allowed but not embedded into assignments or instruction; limited intentional exposure	“Did not have AI-related assignments other than we could use it.”

 

Faculty Survey

Faculty responses revealed meaningful variation in how AI was integrated across courses but converged on several core patterns (see Table 8). In organizational behavior, AI was deliberately embedded into course design through revised learning objectives, structured assignments, and explicit policies emphasizing responsible use and transparency. This approach positioned AI as a tool for augmentation, supporting idea generation and refinement rather than replacement, while also fostering open discussion and norm setting around appropriate use. Similarly, the internship course incorporated AI through iterative, applied activities (e.g., resume refinement, mock interviews), with instructor scaffolding and feedback shaping how students engaged with AI in practice. Consistent with student feedback, the employee training course reflected a more permissive but less structured approach, where AI use was allowed but not systematically integrated into assignments. Across courses, faculty identified key facilitators and barriers to integration. Effective implementation was associated with clear instructional scaffolding, alignment with course objectives, and active learning strategies that embedded AI into meaningful tasks. Barriers noted by faculty included time constraints, variability in faculty expertise and comfort with AI, and uncertainty around best practices.

 

Table 8

Faculty AI Integration Themes by Course

Domain	Organizational behavior	I-O internship	Employee training	Cross-course insight
Integration approach	Fully embedded into objectives, assignments, and policy	Integrated through applied, iterative activities	Permitted but not embedded in coursework	Degree of structure varies substantially
Instructional design	Clear scaffolding (prompt-building, revision cycles, transparency requirements)	Guided practice (mock interviews, resume refinement, AI-assisted feedback)	Minimal scaffolding; primarily optional use	Structured design drives engagement and learning
Role of AI	Augmentation tool (brainstorming, organizing, refining—not replacing work)	Skill-development partner (career prep, interviewing, job materials)	Utility tool (optional support, limited instructional framing)	Framing influences how students use AI
Student engagement	High engagement via interactive and flexible activities	Active engagement through applied tasks and feedback loops	Lower engagement due to lack of structured use	Engagement tied to integration depth
Faculty framing	Explicit emphasis on responsible use and transparency	Encouraged use within guided activities	Neutral/permissive stance	Norm-setting shapes student attitudes and behavior
Challenges	Time constraints; balancing instruction with evolving tools	Need for ongoing refinement of activities	Low perceived value for certain tasks (e.g., summarization)	Faculty capacity and clarity are key constraints
Future directions	Expand structured integration and assessment of AI competencies	Continue refining applied activities and scaling integration	Increase intentional integration into assignments	Shift toward competency-based AI integration

 

Summary Insight

Taken together, these findings suggest that AI-integrated coursework is effective in developing applied, task-oriented skills but may be less effective in fostering higher order competencies without deliberate instructional support. Most importantly, the results indicate that how AI is integrated—through structured, intentional design—plays a central role in shaping both student outcomes and perceptions. 

Future Guidance: Lessons Learned and Best Practices

The findings from this project highlight several practical considerations for faculty integrating AI into I-O psychology coursework. Across both student and faculty perspectives, a consistent pattern emerged: AI integration is most effective when it is intentionally designed, structured, and aligned with competency development rather than treated as optional or ad hoc.

First, one of the clearest lessons is that simply permitting AI use is insufficient. Courses that treated AI as optional saw lower perceived relevance, confidence gains, and engagement, whereas structured integration into assignments and learning objectives produced stronger outcomes. This indicates that AI should be embedded directly into instructional design through activities that require students to actively use, critique, and refine AI-generated outputs rather than treating it as an optional tool.

Second, students readily develop basic AI skills (e.g., content generation), but more complex competencies (e.g., human-centered judgment and strategic integration) require explicit scaffolding. Faculty should design activities that require students to justify AI-assisted decisions, evaluate outputs for accuracy and bias, and iteratively refine their use of AI. These approaches reinforce human oversight and support development of higher order skills.

Third, aligning AI activities with real-world I-O tasks also appears critical. Courses that incorporated applied assignments, such as resume development, training design, or proposal refinement, were perceived as more valuable and engaging. Situating AI use within authentic professional contexts helps students understand how these tools support, rather than replace, domain expertise and improves transferability to applied settings.

Fourth, from the faculty perspective, uncertainty around how to begin integrating AI was a primary barrier. Providing ready-to-use resources (e.g., assignment templates, example prompts, and repositories of AI-integrated activities) may reduce this barrier and facilitate adoption. Clear guidelines for ethical AI use can further support implementation without requiring faculty to develop materials independently.

Fifth, faculty development is also essential, as variability in expertise and comfort with AI remains a challenge. Workshops that include general AI overviews, practical classroom applications, and discipline-specific examples may help build confidence. Effective facilitation requires that faculty establish a baseline level of proficiency with AI tools and their applications. Faculty must be proactive in developing this competence to responsibly guide students in their use. Importantly, faculty do not need to be technical experts; rather, they can serve as facilitators who guide students in developing effective and responsible AI use. Similar to how instructors teach students to use tools like SPSS or R without requiring expertise in their underlying codebase, faculty can guide AI use by focusing on application, interpretation, and critical evaluation.

Finally, given the rapid evolution of AI, a competency-based approach is more sustainable than focusing on specific tools. Emphasizing transferable skills (e.g., critical evaluation, ethical reasoning, and adaptive learning) ensures that students can apply these capabilities across changing technologies and contexts.

These findings offer insight not only into the development and implementation of AI competencies within one I-O graduate program, but also into how students and faculty respond to these efforts in practice. As AI becomes increasingly embedded in organizational contexts, there is a growing need for I-O practitioners who can not only leverage these tools but also critically evaluate their outputs, apply them responsibly, and integrate them effectively into organizational systems.

By presenting this case study, the current paper provides a practical, competency-based framework that other I-O programs can adapt when integrating AI into their curricula. Although specific implementation strategies may vary across programs, the broader principles outlined here—such as structured integration, alignment with applied tasks, and emphasis on transferable competencies—offer a foundation for curriculum development.

More broadly, this work is intended to contribute to an ongoing conversation within the field regarding how best to prepare future I-O psychologists for an AI-integrated workplace. As programs continue to experiment with different approaches, sharing these experiences can help establish best practices, identify common challenges, and support more consistent and effective training across institutions.

References

Aguinis, H., Beltrán, J. R., & Cope, A. (2024). How to use generative AI as a human resource management assistant. Organizational Dynamics, 53(1), Article 101029.

Ardichvili, A., Dirani, K., Jabarkhail, S., El Mansour, W., & Aboulhosn, S. (2024). Using generative AI in human resource development: An applied research study. Human Resource Development International, 27(3), 388–409. https://doi.org/10.1080/13678868.2024.2337964

Becker, S. (2022). A fast-growing segment of psychology is landing grads jobs in corporate America. Fortune. https://fortune.com/education/articles/a-fast-growing-segment-of-psychology-is-landing-grads-jobs-in-corporate-america/

Carmichael, M. (2024). People are worried about the misuse of AI, but trust it more than humans. Ipsos. https://www.ipsos.com/en-us/people-are-worried-about-misuse-ai-they-trust-it-more-humans

Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Nature Humanities and Social Sciences Communications, 10. https://doi.org/10.1057/s41599-023-02079-x

Ekuma, K. (2024). Artificial Intelligence and automation in human resource development: A systematic review. Human Resource Development Review, 23(2), 199–229. https://doi.org/10.1177/15344843231224009

Goldstein, I. L., & Ford, J. K. (2002). Training in organizations: Needs assessment, development, and evaluation (4th ed.). Wadsworth/Thomson Learning.

Graßmann, C., & Schermuly, C. C. (2021). Coaching with Artificial Intelligence: Concepts and capabilities. Human Resource Development Review, 20(1), 106–126. https://doi.org/10.1177/1534484320982891

Gutierrez, S., & Landers, R. N. (2024). How to survive the AI revolution in HR: Culture change and immediate action. SIOP White Paper. https://www.siop.org/resource/how-to-survive-the-ai-revolution-in-hr-culture-change-and-immediate-action/

Karataş, F., Eriçok, B., & Tanrikulu, L. (2025). Reshaping curriculum adaptation in the age of artificial intelligence: Mapping teachers’ AI-driven curriculum adaptation patterns. British Educational Research Journal, 51, 154–180. https://doi.org/10.1002/berj.4068

Lin, H., & Chen, Q. (2024). Artificial intelligence (AI)-integrated educational applications and college students’ creativity and academic emotions: Students’ and teachers’ perceptions and attitudes. BMC Psychology, 12(1), 487. https://doi.org/10.1186/s40359-024-01979-0

Society for Industrial and Organizational Psychology. (2023). Considerations and recommendations for the validation and use of AI-based assessments for employee selection. https://www.siop.org/about-siop/siop-statements/considerations-and-recommendations-for-the-validation-and-use-of-ai-based-assessments-for-employee-selection-january-2023/

Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(15). https://doi.org/10.1186/s41239-024-00448-3

Weiner, J. A., Tippins, N., Landers, R., Ryan, A., Handler, C., & Munson, L. (2024, April). Ethical AI-based assessment in practice. Panel presented at the Society for Industrial and Organizational Psychology Annual Conference, Chicago, IL, April 17-20.

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Appendix A

Subject Matter Expert (SME) Interview Protocol

Introduction

• Purpose: Introduce yourself, clarify the study’s objectives (i.e., how AI usage in HR/IO roles can shape the AI competencies that I-O psychology students need).
• Confidentiality and consent: Explain confidentiality measures and request permission to record & transcribe.
• Interview structure: Mention the semi-structured format; there are main questions plus potential follow-ups.

1. Role & AI responsibilities

Main question

1. “Could you briefly describe your role and how you currently incorporate AI tools or techniques into your daily tasks or responsibilities?”

Follow-up questions

• “Which specific tasks do you handle that involve AI, and how frequently?”
• “How did AI first become part of your work routine? Was it your idea, or was it part of an organizational initiative?”

2. Organizational objectives & AI goals

Main question
2. “Which organizational objectives or challenges are you aiming to solve by using AI, and why did these become priorities?”

Follow-up questions

• “Could you give an example of a key pain point AI helps address (e.g., time savings, improved accuracy, better candidate matching)?”
• “What factors led leadership to invest in AI solutions (e.g., cost reduction, strategic differentiation, or something else)?”

3. Tools & methods

Main question
3. “What AI tools, platforms, or methods (e.g., chatbots, predictive analytics, machine learning) do you rely on most often, and for which specific tasks?”

Follow-up questions

• “Is your choice of AI tools shaped by corporate policy or personal preference?”
• “Have you tried multiple AI solutions? What made you settle on your current toolset?”

4. Developing AI prompts & providing context

Main question
4. “How do you develop prompts or queries for AI systems (like ChatGPT) to ensure you get useful, context-specific results? Could you give examples?”

Follow-up questions (prompt engineering focus)

• “How do you refine or iterate on your initial prompts to get more accurate or actionable insights from AI?”
• “What strategies do you use to ensure the AI receives enough context (like data, background info, or constraints) to generate meaningful results?”
• “Have you developed or discovered any prompting best practices (e.g., structured formatting, specific keywords) that reliably improve AI output quality?”
• “Do you find yourself reusing or standardizing certain prompt structures, and if so, how do you document or share them within your team?”

5. Automation versus manual tasks

Main question
5. “Which parts of your role are now automated or partially automated through AI, and which tasks do you still prefer to handle manually?”

Follow-up Questions

• “What categories or ‘buckets’ of tasks (e.g., communication, data aggregation) do you find easiest to automate?”
• “Are there areas where you deliberately avoid using AI, and if so, why?”

6. Verification & governance

Main question
6. “When AI provides recommendations or analyses, how do you verify the information? Do you have any governance frameworks or checks in place to ensure accuracy and compliance?”

Follow-up questions

• “What steps do you take if the AI’s suggestions look questionable? Who do you consult or what external data sources do you rely on?”
• “Are there guidelines or protocols (organizational or personal) that outline how to validate AI outputs?”

7. Organizational context & AI policy

Main question
7. “Is AI use in your function personally driven, or is it institutionally encouraged? What is your organization’s official stance on AI tools?”

Follow-up questions

• “Do you feel organizational support (e.g., resources, training, top management buy-in) for AI usage?”
• “Are there formalized AI governance policies or disclaimers you must follow?” 

8. Ethical, bias, & fairness concerns

Main question
8. “Have you encountered concerns around ethics, bias, or fairness with AI tools? How do you address potential biases or privacy issues when using AI?”

Follow-up questions

• “Could you describe a real incident or close call where AI recommendations seemed biased or ethically problematic?”
• “How do you handle ambiguity or incomplete data in your prompts to prevent AI from producing irrelevant or misleading responses?”

9. Required skills & competencies

Main question
9. “What new skills or competencies did you (or your team) need to develop to effectively interpret, manage, or implement AI outputs in your HR/IO context?”

Follow-up questions

• “Did you or others undergo specific training or certification programs?”
• “Which skills (technical, analytical, ethical, communication) have proven most critical to using AI effectively?”

10. Measuring impact & AI benefits

Main question
10. “How do you measure the impact of AI on your performance or productivity? Are there specific KPIs or metrics you track to gauge success?”

Follow-up questions

• “How has AI changed your job performance, if at all (time savings, better accuracy, improved insights)?”
• “Which benefits have you observed from integrating AI (efficiency, accuracy, deeper insights)?”

11. Challenges & change management

Main question
11. “Did you face any hurdles (technical, cultural, or ethical) when first introducing AI in your function, and how did you overcome stakeholder pushback?”

Follow-up questions

• “Were there misunderstandings or resistance from employees about AI’s role?”
• “What strategies did you find effective for gaining buy-in from leadership or colleagues?” 

12. AI–human collaboration

Main question
12. “How do your own expertise and judgment combine with AI’s outputs? Do you see AI more as an assistant offering suggestions or as a decision-maker?”

Follow-up questions

• “When do you rely heavily on AI vs. when do you override its recommendations?”
• “In tasks like selection, do you treat AI input as one data point among many, or is it decisive?” 

13. Future outlook & evolution

Main question
13. “Which additional aspects of your role (or the organization) do you foresee could be automated soon, and how do you envision AI evolving in your field over the next few years?”

Follow-up questions

• “Do you predict new opportunities or concerns might emerge (for instance, advanced analytics, robotics integration, or deeper personalization)?”
• “Could you see a shift from more operational tasks to purely strategic tasks once more AI tools are in place?” 

14. Best practices for prompting AI

Main question
14. “Do you have any best practices or lessons learned for crafting better AI prompts or setting up AI queries, especially to ensure context and accuracy?”

Follow-up questions

• “Could you give a specific example of a well-crafted prompt you used and why it worked better than a simpler version?”
• “How do you refine or iterate on prompts? Do you do it alone or collaboratively with a team?”

15. Advice for I-O students

Main question
15. “What guidance or lessons learned would you share with I-O students who want to integrate AI into their future roles? Any pitfalls to avoid or quick wins to pursue?”

Follow-up questions

• “Are there certain AI competencies (ethical oversight, data analytics, coding basics, etc.) they should prioritize?”
• “Do you see more synergy between AI and I-O psychology in the future?”

Closing

• Thank the SME for their time and insights.
• Confirm if it’s alright to contact them for follow-up questions or clarifications.
• Next steps: Briefly outline how their input will shape the AI competencies to be taught in I-O programs.

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Appendix B

Subject Matter Expert (SME) Competency Survey

 

Introduction & Welcome!

 

Thank you for participating in this study. Your insights will help us determine which AI-related competencies are most critical for I-O psychology graduate students to learn before entering the workforce. The findings will be used to guide curriculum development at UTA and ensure that future I-O professionals are prepared to work effectively with AI-enhanced tools and environments.

 

Specifically, we are not asking you to assess what you personally do in your job but rather what new I-O psychology graduates should know and be able to do on day one of an entry-level role involving AI-related work.

 

Your expertise is instrumental in shaping a curriculum that reflects the demands of real-world practice.

 

Survey Instructions

 

Please read these instructions carefully. You will be presented with a set of 58 task statements and illustrative behaviors grouped under high-level competency areas related to AI use in I-O psychology. For each item, you will rate two things using the following 0–5 scales:

 

Scale 1: Instructional Priority

How important is it that this task be taught or developed during a graduate I-O psychology program?

• 0 = Not at all important – Does not need to be taught in a graduate program
• 1 = Slightly important – Might be helpful, but low priority for instruction
• 2 = Moderately important – Some relevance; could be introduced but not emphasized
• 3 = Important – Should be covered at a basic level in the curriculum
• 4 = Very important – Should be taught in detail and practiced in class
• 5 = Essential – Core instructional priority; graduates must learn this to be prepared for an entry-level position in the field

 

Scale 2: Proficiency at Entry

What level of proficiency should a graduate possess upon entering the workforce to perform this task effectively?

• 0 = No proficiency – Not expected to perform this task at entry-level
• 1 = Basic awareness – Can recognize or describe the task, but cannot perform it independently
• 2 = Introductory skill – Can perform the task with significant guidance or support
• 3 = Intermediate proficiency – Can perform the task with minimal guidance or oversight
• 4 = Advanced proficiency – Can perform the task independently and accurately
• 5 = Expert-level proficiency – Can execute, teach, and improve this task without assistance (typically not expected at entry level**)
• **Note: Most tasks will not require a level 5 at entry.

 

Please be realistic based on what you expect from a new graduate in an applied setting. Estimated time to complete: 20-30 minutes; Please answer honestly and based on your professional judgement. All responses are anonymous and will be used in aggregate only. You may skip any items if they do not apply to you or are not relevant.

Job title

 

 

Organization/department

 

 

Years of experience

 

 

 

Education level

1. Bachelors
2. Masters
3. PhD
4. Other

 

Field of study (I-O psychology, human resources, data science, business etc.)

 

 

How long have you utilized AI/AI tools/large language models (LLMs) in completing work tasks?

1. 0–1 year
2. 1–2 years
3. 2–3 years
4. 3–4 years
5. 4+ years

 

How often do you utilize AI/AI tools/LLMs in completing work tasks?

1. Never
2. Sometimes
3. Most of the time
4. Everyday

 

Instructional Priority [Not at all Important – Essential]

Proficiency at Entry [No Proficiency – Expert-Level]

0

1

2

3

4

5

0

1

2

3

4

5

1. Compare capabilities and limitations across multiple AI platforms to select tools that best align with user needs and organizational constraints.

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2. Create and implement standard operating procedures for AI tool usage to ensure consistent, secure, and effective integration in program workflows.

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3. Apply foundational knowledge of AI systems—such as how LLMs process language—to choose tools suited to specific output requirements.

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4. Operate organizationally licensed AI tools (e.g., private LLMs, Copilot) within secure environments and in accordance with internal compliance protocols.

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5. Pilot emerging AI technologies under restricted licenses, document functional outcomes, and share findings with key stakeholders.

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6. Select and deploy AI tools based on both productivity potential and adherence to task-specific security requirements.

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7. Use external AI tools (e.g., Copilot, Gemini) when internal tools are limited, particularly for automating spreadsheet functions or debugging simple scripts.

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8. Write or modify simple scripts (e.g., Excel macros) to reduce repetitive tasks and embed AI functionality into workflows.

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9. Prompt AI tools to identify and troubleshoot code or runtime errors, accelerating debugging compared to manual resolution.

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10. Generate quick solutions to common technical challenges—like formulas or syntax issues—by leveraging AI’s rapid response capabilities.

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11. Communicate the value of AI tools by aligning them with organizational goals and motivating teams to embrace new solutions.

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12. Pilot AI-driven process improvements and share early wins to reduce resistance and demonstrate feasibility.

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13. Act as an internal advocate by informally mentoring colleagues and sharing best practices for AI integration, even without a formal policy.

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14. Highlight unique human capabilities—like critical thinking and contextual judgment—to complement AI use and reinforce professional value.

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15. Promote broader AI adoption by packaging success stories, prototypes, or client-ready use-cases into presentations or strategic proposals.

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16. Use time saved through AI tools to actively engage with colleagues and stakeholders, such as mentoring, coaching, or collaborating on projects.

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17. Partner with peers across departments or regions to provide accessible AI support, building capacity and trust in shared tools and practices.

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18. Draft training content and learning modules by feeding structured outlines and goals into AI tools, then refining outputs for clarity and alignment.

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19. Use AI to generate email drafts, templates, and tone-adjusted communications, then edit them for audience-appropriate professionalism.

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20. Prompt AI to assist with initial brainstorming for visual design or storytelling elements, especially when creative direction is unclear.

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21. Convert complex ideas or frameworks (e.g., road maps, leadership models) into concise summaries using AI, then integrate into presentation slides.

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22. Leverage AI writing tools to polish existing content—scripts, job descriptions, or lesson plans—while retaining human tone and intent.

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23. Use AI to synthesize first drafts of long-format content (e.g., workshops, e-learning modules), which are later customized for specific groups.

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24. Employ AI to rapidly draft or storyboard multimedia elements (e.g., narrated training videos), allowing faster iteration in design cycles.

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25. Maintain quality by reviewing AI-generated drafts for style, grammar, and content consistency before finalizing deliverables.

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26. Identify and correct inaccuracies in AI-generated outputs by comparing them against independently verified data or trusted external sources.

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27. Review AI-generated proposals or content and iteratively adjust prompts until results meet organizational standards for tone, structure, and relevance.

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28. Conduct manual quality checks—including data triangulation or parallel analysis—before distributing or acting on AI-assisted outputs.

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29. Use chain-of-thought tools or model reasoning views to diagnose misinterpretations in AI responses and revise inputs accordingly.

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30. Design assessments or prompts that require candidates or users to explain their reasoning behind AI-generated responses, ensuring true understanding and not blind acceptance.

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31. Seek out and experiment with new AI tools or features, adjusting workflows as technologies evolve.

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32. Maintain a regular practice of prompt engineering, noting what works and updating strategies based on trial and error.

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33. Develop reusable processes and prompt templates that accelerate adoption of AI in emerging organizational tasks.

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34. Combine hands-on experimentation with structured upskilling (e.g., courses in coding, statistics, AI fundamentals) to increase technical fluency.

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35. Demonstrate learning agility by proactively unlearning outdated search habits and refining inputs to generate higher-quality AI responses.

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36. Screen inputs for confidential or personally identifiable information (PII) and redact sensitive fields before submission to AI tools.

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37. Consult with security or governance teams when deploying new AI tools to ensure compliance with privacy, safety, and storage protocols.

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38. Apply prompt-engineering standards that minimize unnecessary data exposure while still achieving accurate AI responses.

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39. Audit AI outputs for potential bias or risk and intervene when automated suggestions may lead to unfair or noncompliant outcomes.

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40. Participate in developing or improving centralized AI data pipelines to ensure responsible data access and usage across departments.

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41. Conduct needs assessments and stakeholder interviews to inform AI use cases, ensuring AI-generated outputs are aligned with real organizational priorities.

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42. Synthesize domain-specific research (e.g., IO psychology, organizational behavior) alongside AI outputs to ensure accurate, evidence-based insights.

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43. Translate qualitative constructs (e.g., sentiment, psychological experience) into quantifiable variables for AI input to preserve human relevance.

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44. Apply foundational data-science concepts to evaluate AI-generated outputs and collaborate effectively with technical teams.

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45. Perform initial data cleaning and organize diverse data sources before feeding into AI tools to improve output quality and relevance.

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46. Distinguish between tasks that benefit from AI automation and those that require human oversight, reserving complex or sensitive work for manual handling.

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47. Use AI to generate first drafts or options, then conduct thorough human review to ensure quality, contextual accuracy, and ethical alignment.

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48. Maintain a human-in-the-loop policy for decision-making, using AI outputs as advisory input while preserving final judgment for people leaders.

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49. Preserve authenticity and human connection in communications by personalizing AI-generated content, especially in sensitive situations.

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50. Advocate for human expertise by highlighting intuition, ethical reasoning, and contextual awareness in conversations about AI adoption.

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51. Design prompts that include key details such as role, audience, and desired output to generate relevant and actionable AI responses.

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52. Begin prompts with a clear action verb and embed specific context (e.g., style, tone, format) to reduce ambiguity and increase precision.

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53. Test and refine prompt wording by reviewing AI-generated results, iterating until the outputs meet the intended purpose.

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54. Break down lengthy or complex inputs into smaller parts to avoid exceeding AI context windows and ensure complete, logical responses.

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55. Develop a library of standardized, domain-specific prompts to streamline recurring tasks and improve team-wide consistency.

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56. Track emerging industry trends to inform strategic workforce planning and identify roles at risk of automation or transformation through AI.

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57. Clarify the purpose and intended outcome of AI-assisted tasks to ensure outputs contribute to broader organizational goals.

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58. Maintain and update a structured repository of AI use cases (e.g., Power BI dashboard) to support long-term planning, decision-making, and cross-functional knowledge sharing.

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What AI skills or competencies do you believe are missing from this list? Comments/suggestions:

 

 

Please describe any specific tasks or projects where you’ve successfully applied AI.

 

 

Do you have concerns or barriers that impact AI use in your role?