Enhancing Practical and Ethical AI Use in Online Doctoral Education through Targeted Webinars

Abstract

This pilot study examined gaps in doctoral students’ and faculty members’ effective and ethical use of Generative AI (GAI) at a large online university in the United States. It evaluated whether targeted webinars could improve understanding, confidence, and intended use of GAI in academic and research contexts.

Using a multiple-methods case study design, the study combined quantitative pre- and post-webinar surveys with qualitative open-ended responses. Descriptive statistics captured changes in understanding, confidence, and use, while thematic analysis identified patterns in expectations, perceived value, and concerns.

Preliminary faculty findings suggest improved understanding of GAI applications and ethics after the webinars, along with greater confidence and intent to integrate GAI into teaching and research. Students expressed strong interest in ethical use and research support but remained concerned about academic integrity and unclear institutional guidance. Ethical uncertainty persisted as the strongest barrier across both groups.

This study contributes to emerging research on GAI literacy in doctoral education by piloting a scalable training intervention to strengthen ethical and effective use. The findings provide early evidence that targeted instruction can improve faculty readiness while underscoring the need for clearer institutional guidance and sustained support for responsible use.

Keywords: Generative AI (GAI), Large Language Models (LLMs), AI Literacy, AI Ethics, Faculty Development, Doctoral Education

Overview of the Problem

Within the university’s doctoral programs, students and faculty showed limited understanding of how to use GAI effectively in academic and professional work, as reflected in baseline survey data. This gap restricts use of GAI for research, writing, and problem-solving while increasing uncertainty about acceptable academic practice. Faculty face challenges integrating GAI into instruction and research workflows, and students struggle to determine how it should support dissertations, coursework, and professional development. Without structured training, both groups risk underusing or misusing these tools, slowing academic progress and AI competency development.

Theoretical Framework

This study was informed by Expectancy-Value Theory (EVT), which describes how individuals’ expectations for success and the value they place on a task shape their motivation and engagement (Wigfield, 1994; Wigfield & Eccles, 2000). Within EVT, expectancy refers to individuals’ beliefs about their capability to perform a task, and subjective task value reflects the perceived importance, usefulness, or interest associated with that task. A third component, cost, captures the perceived effort, potential risks, and competing demands that may deter engagement.

EVT is appropriate for examining how students and faculty approach GAI because their willingness to use these tools depends on confidence in their ability to use them effectively (expectancy), the extent to which they view GAI as relevant or helpful for academic work (value), and concerns about ethical use, accuracy, or the time required to verify outputs (cost). These constructs guided the development of the survey items and provided a framework for interpreting how participants described their understanding, confidence, and perceived barriers. The webinar series was also designed with these elements in mind by demonstrating practical applications of GAI to build expectancy, illustrating its usefulness to strengthen value, and addressing ethical and procedural concerns to reduce perceived cost.

Purpose of the Study

The purpose of this applied research study was to design and implement an interactive webinar to improve doctoral students’ and faculty members’ understanding and use of GAI. As a pilot effort and preliminary step toward further research, it evaluated whether the webinar addressed knowledge gaps and strengthened ethical, confident application of GAI in academic work.

Research Questions

1. How effective are topic-specific webinars in improving doctoral students’ understanding and use of GAI in their academic work?
2. How effective are topic-specific webinars in enhancing faculty members’ ability to integrate GAI into their teaching and research?
3. Which aspects of GAI do doctoral students and faculty find most challenging before and after attending the webinars?
4. How do students and faculty apply their GAI-related knowledge following the webinars?

Significance of the Study

This study addressed a gap in effective GAI use among doctoral students and faculty. Through targeted webinar training, it sought to strengthen ethical, productive use in academic work. Improved GAI proficiency may support students’ research and academic progress, inform faculty development and policy, and guide responsible AI literacy initiatives in higher education.

Literature Review

Artificial intelligence is reshaping higher education through personalized learning, workforce preparation, and curriculum design. AI-enabled systems support more adaptive learning environments by tailoring content, monitoring performance, and providing feedback that can improve engagement and outcomes (Imron et al., 2024; Slimi, 2023; Song, 2024). As AI becomes more embedded in academic settings, institutions are revising curricula to help students develop AI-related and cross-disciplinary competencies aligned with workforce demands (Slimi, 2023; Morandini et al., 2023). Many are also incorporating AI literacy frameworks and instruction to address both practical use and ethical considerations (Daly et al., 2024; Magrill & Magrill, 2024).

Despite these developments, concerns about reliability, bias, and appropriate use persist among faculty and students (Daly et al., 2024; Kallunki et al., 2024). Limited training contributes to this uncertainty, reinforcing the need for stronger institutional support and structured learning opportunities (Almaraz-López et al., 2023; Mohamad & Naslan, 2024). Ethical issues—including privacy, algorithmic bias, plagiarism, authorship, and transparency—remain central, and institutions continue to address them inconsistently (Slimi, 2023; Farangi et al., 2025). In doctoral contexts, these concerns may be especially consequential because they affect scholars’ sense of agency and academic voice (Rafi & Amjad, 2025).

These issues are especially relevant in doctoral education, where dissertation writing already demands substantial cognitive and linguistic effort. Many doctoral scholars report limited preparation for this level of academic work, which can make integrating GAI more difficult (Rafi & Amjad, 2025). Although GAI can assist with writing mechanics, idea development, and argument organization, it can also introduce risks to critical thinking and authorship when poorly understood (Rafi & Amjad, 2025). Researchers therefore emphasize structured AI literacy, clearer supervisory guidance, and intentional institutional support so doctoral writers can use GAI responsibly while retaining ownership of their scholarship. These insights align with the goals of the present intervention.

Research Design and Methodology

This applied research study used a multiple-methods case study design to conduct a pilot evaluation of changes in participants’ understanding and use of GAI following webinar-based instruction. Pre- and post-webinar surveys assessed GAI usage, confidence, ethical awareness, and perceived challenges. Descriptive statistics were used to analyze Likert-scale responses, while thematic analysis was applied to open-ended responses to identify patterns in participants’ experiences.

Population and Sampling Procedures

All actively enrolled doctoral students and faculty with active teaching status in August 2025 were invited to participate in an anonymous survey. Email invitations directed participants to a pre-webinar survey administered through Microsoft Forms. Forty-nine students and twenty-six faculty completed the pre-webinar survey. Webinar registration through Zoom provided attendee email addresses, which were used to distribute the post-webinar survey approximately four weeks later. One student and eight faculty members completed the post-webinar survey.

GAI Webinars

The webinars emphasized GAI’s potential in doctoral research and higher education while centering ethical, effective use. They highlighted applications such as personalized learning, research support, administrative efficiency, and accessibility, while stressing academic integrity, data privacy, and responsible use of AI-generated content. This framing positioned GAI as a supplement to, not a replacement for, critical thinking and original scholarly voice.

The webinars also presented explicit guidelines for ethical and effective GAI use. Students were encouraged to disclose use, cite AI-generated content appropriately, and evaluate outputs critically. Faculty were encouraged to set clear expectations and promote critical engagement. Practical strategies included prompt engineering, brainstorming, summarization, and language refinement, alongside cautions about overreliance, plagiarism, and entering sensitive information. The sessions emphasized iterative use, task-tool fit, and continued scholarly rigor.

Instrumentation

The student and faculty surveys were developed by the authors to collect demographic information and measure GAI usage, understanding, confidence, and perceptions. Each survey included demographic items and a set of identical Likert-scale questions for both groups across the pre- and post-webinar administrations. Likert-scale items asked participants to rate their agreement on a five-point scale ranging from 1 (strongly disagree) to 5 (strongly agree), with 3 representing a neutral response.

The qualitative components differed across survey administrations. The pre-webinar surveys asked two open-ended questions related to what participants hoped to learn from the webinars and the challenges they faced when using GAI. The post-webinar surveys included eight open-ended questions that probed participants’ perceptions of the webinar and any changes in their GAI usage, confidence, or challenges since attending.

The questionnaires were entirely original, developed specifically for this study, and had not undergone prior validation. Due to research time constraints, organizational demands requiring an expedited timeline, and difficulty securing participants, a pilot study to validate instrumentation was not conducted. As a result, internal consistency was not examined through Cronbach’s alpha. This limitation introduces the possibility of measurement error and may affect the consistency of Likert-scale responses.  Findings should be interpreted with appropriate caution.

Procedures

Demographic data were used to categorize Likert-scale responses by prior webinar attendance, age, gender, primary language, and doctoral credit hours. Descriptive statistics were computed for each item. Qualitative responses were analyzed using thematic analysis, involving familiarization, coding, theme development, and refinement. Only pertinent data and observations of interest are presented within the results due to space limitations.

Limitations

Student post-webinar quantitative data were excluded because only one student completed the survey. As a pilot study with limited post-webinar participation, findings should be interpreted as preliminary and are not intended to be broadly generalizable. Differences in baseline GAI knowledge may limit generalizability, and reliance on self-report introduces potential bias; larger follow-on research is needed to test effects with more robust sampling and longitudinal measurement.

Results

Students

Counts of student-reported GAI uses and challenging GAI topics are shown in Tables I and II. Twice as many students reported using GAI for research (count of = 40) than for classwork (count of = 19). The most challenging topic was ethics and cheating (count of = 23), followed by research (count of = 16); classwork was the least challenging (count of = 8).

Table I

Student Reported Uses of GAI (N = 49)

Table II

Most Challenging GAI Topics Reported by Students (N = 49)

Aggregate means and standard deviations for Likert-scale items appear in Table III. Students reported the highest agreement with understanding the ethical considerations of using GAI (M = 3.96) and the lowest agreement with their intention to use GAI for classwork (M = 3.14) and their confidence in doing so (M = 3.20).

Table III

Student Ratings on GAI Likert-Scale Items (N = 49)

Table IV shows student means and standard deviations by number of GAI webinars previously attended. Students with three or more prior webinars demonstrated the strongest understanding of prompt engineering and ethical considerations (both M = 4.67) and the highest confidence in producing useful outputs (M = 4.67). However, they reported the lowest intention to use GAI for classwork (M = 2.67). Students with no prior webinar experience generally demonstrated weaker understanding of GAI-related concepts but expressed somewhat higher intent to use GAI for classwork.

Table IV

Student GAI Ratings by Number of Prior Webinars Attended

The student pre-webinar survey asked what students hoped to learn and what challenges they faced in using GAI. Ninety-four excerpts were coded, yielding three themes: strong expectations for learning about GAI, high value placed on ethical use and research support, and notable perceived costs tied to ethical ambiguity, academic integrity, and uncertainty about effective use.

Students generally believed they could learn to use GAI but described a steep learning curve and uncertainty about where to begin. Many were unclear about what the university considered acceptable and how much GAI support was permissible. They saw GAI as useful for academic and professional productivity yet unfamiliar in practice. Reported costs centered on plagiarism, cheating, overreliance, authenticity, source verification, misinformation, and the effort required to develop prompt-engineering skill. Several comments specifically reflected a desire to use GAI productively without compromising integrity.

Faculty

Pre- and post-webinar faculty reports of GAI use and challenging topics are shown in Tables VI and VII. Faculty reported similar levels of GAI use before and after the webinar. Ethics was the most challenging topic in both surveys. A slightly larger share of faculty identified class discussions as a challenging area post-webinar.

Table VI

Faculty Reported Uses of GAI Before and After the Webinar

Table VII

Most Challenging GAI Topics Reported by Faculty Before and After the Webinar

Aggregate faculty means and standard deviations appear in Table VIII. Faculty demonstrated higher mean scores on all post-webinar items except intention to use GAI in research (M = 4.08 pre-webinar; M = 3.88 post-webinar). Notably, faculty expressed increased concern about being penalized for ethical misuse of GAI after the webinar (M = 2.68 pre; M = 3.13 post).

Table VIII

Faculty Ratings on GAI Likert-Scale Items Before and After the Webinar (N = 25)

Table IX presents pre-webinar means by prior webinar attendance. Faculty with three or more previous webinars reported the highest mean scores across nearly all items, including understanding GAI’s role in teaching (M = 4.56), confidence in using GAI for teaching and research (M = 4.22; M = 4.33), and intent to use GAI in research (M = 4.56). They also expressed the lowest concern about being penalized for misuse (M = 2.44). Data for post-webinar comparisons by attendance were not analyzed due to insufficient responses.

Table IX

Faculty GAI Ratings by Number of Prior Webinars Attended

The faculty pre-webinar survey included two open-ended questions about what faculty hoped to learn and the challenges they faced in using GAI. Twenty-four excerpts were coded, yielding four themes: improving efficiency, ensuring ethical use, enhancing student support, and clarifying university policy.

Faculty expressed interest in using GAI to improve efficiency in teaching and research and wanted a better understanding of available tools. Some described using AI to generate follow-up questions or visuals for discussions, while others wanted guidance on selecting appropriate tools for specific academic tasks.

Ethical use and academic integrity were also prominent concerns. Faculty sought clearer boundaries for acceptable use, strategies for coaching students, and ways to recognize misuse or AI-generated content, while also noting ambiguity in university expectations.

Clarifying institutional policy emerged as an additional theme, with faculty describing limited explicit guidance as a barrier to confident, ethical use of GAI.

The faculty post-webinar survey yielded 27 excerpts across three themes: increased value and expectancy for using GAI, continued ethical and evaluative concerns, and workload-related costs. Faculty reported greater confidence using GAI for tasks such as refining feedback and organizing ideas, but uncertainty remained about evaluating student work. They valued time savings, improved feedback, and expanded teaching resources while still noting the time required to learn tools and verify output.

Discussion

This pilot study evaluated whether a webinar-based intervention could strengthen doctoral students’ and faculty members’ understanding and ethical use of GAI. Several limitations shape interpretation of these preliminary findings. Because only one student and eight faculty completed the post-webinar survey, meaningful conclusions about change over time were limited, and interpretation relies largely on pre-webinar data. The study also used self-report data collected shortly after the webinar, so it does not show how GAI use may evolve with additional experience or institutional guidance. Future research should use larger samples, longer follow-up periods, and stronger measurement to examine adoption over time and test scalable GAI literacy models in doctoral programs.

Pre-webinar findings suggest several patterns in how both groups approached GAI. Students were twice as likely to report using GAI for research as for classwork, whereas faculty reported similar use across teaching and research. For both groups, ethics remained the most challenging topic before and after the webinar. Students’ lower intention to use GAI for classwork may reflect concern about being penalized for misuse, a pressure that may feel less immediate in independent research.

Although students reported similar understanding of how GAI could support classwork and research, they showed relatively low confidence in applying it to classwork. This suggests that knowledge alone was not the main barrier; uncertainty about acceptable use appears to have limited adoption, consistent with EVT’s cost component.

Pre-webinar qualitative findings showed high perceived value of GAI and moderate, increasing expectancy as participants gained experience. At the same time, concerns about ethics, workload, and institutional expectations remained. Faculty post-webinar feedback suggested some growth in confidence, but continued need for clearer guidance and institutional support.

Ethical uncertainty was central for both groups. Participants described concerns about plagiarism, data privacy, authorship, and transparency, consistent with prior research (Slimi, 2023; Farangi et al., 2025). These concerns may limit adoption unless institutions provide clearer policy, training, and communication to support responsible use.

Overall, the findings reflect a doctoral community that is open to GAI but cautious in practice. Participants recognized its potential benefits while asking for clearer boundaries, stronger ethical frameworks, and continued practical skill development. When these supports are in place, GAI is more likely to function as a productive and responsible tool for doctoral learning and faculty scholarship.

Administrative and Governance Implications for Distance Learning

These findings have practical implications for institutional governance of generative AI, responsibility for AI literacy, and development and communication of policy across distributed online programs.

Implications for institutional AI governance

Ethics was the most challenging topic for both groups, and students cited unclear guidelines as a barrier to use. These results point to the need for institution-level AI governance that defines permissible and impermissible uses, disclosure expectations, and baseline privacy requirements, and that is reinforced through aligned training and support resources rather than policy statements alone.

Administrative ownership of AI literacy initiatives

Both students and faculty indicated a need for clearer institutional direction, and faculty explicitly asked what stance the university would take on AI use. This suggests AI literacy should be treated as a managed institutional initiative with defined ownership rather than optional professional development. In large online institutions, this typically requires coordination across academic leadership, teaching and learning functions, and compliance and privacy teams. The webinar model used here offers one approach that can be standardized over time.

Policy development and communication strategies

Participants’ comments indicate that policy must function as operational guidance that reduces ambiguity for learners and instructors. The webinar emphasized disclosure, appropriate citation, output verification, and avoiding sensitive data entry; these practices can be translated into institutional standards and course-level guidance. For distance learning administration, a workable approach includes an institution-wide baseline policy, program-level guidance for discipline-specific use cases, and reusable syllabus or assignment language. Clear pathways for questions and exceptions are also needed.

Scalability considerations for large online institutions

The study positions webinars as a scalable training option, but the results also reflect common constraints in large online settings, including uneven participation, changing tool capabilities, and persistent ethical questions. Scaling this approach requires standardized modules, regular updates as tools and policies evolve, and discipline-relevant examples. Because prior webinar exposure was associated with higher reported understanding and confidence, institutions may benefit from sequenced training and measures that extend beyond immediate post-session perceptions.

Potential Future GAI Webinar Topics for Student Participants

The topics below reflect the most common needs and challenges students identified in the surveys. Each topic aligns with themes in the data and highlights areas where additional support may improve student confidence and skill development.

• Ethical and Responsible GAI Use

Addresses confusion about institutional guidelines, academic integrity, appropriate disclosure, and strategies for avoiding plagiarism and misuse.

• GAI for Research

Supports students in applying GAI to literature reviews, idea generation, research planning, and methodological questions.

• GAI for Writing, Studying, and Classwork

Focuses on day-to-day academic tasks such as drafting, revising, summarizing, and using GAI as a study aid.

• GAI Foundations and Prompt Engineering

Builds essential skills for crafting effective prompts, evaluating AI-generated outputs, and understanding the capabilities and limitations of different tools.

Potential Future GAI Webinar Topics for Faculty Participants

The following topics reflect ongoing needs expressed by faculty and areas where additional training may strengthen teaching and support student learning.

• Ethical GAI Use in Higher Education

Clarifies expectations for transparency, authorship, citation, and acceptable use in coursework and research.

• GAI for Course Design and Classroom Integration

Provides strategies for incorporating GAI into teaching materials, discussions, content creation, and instructional planning.

• Assessment and Student Support in the Age of GAI

Covers evaluating student work that may involve GAI, designing assessments that reflect learning outcomes, and supporting students’ GAI literacy.

• Applied GAI Skill Building for Faculty

Offers practical techniques for prompt engineering, generating feedback, organizing research ideas, and improving teaching efficiency.

Summary

This pilot study examined the effectiveness of targeted webinars in improving doctoral students’ and faculty members’ understanding and ethical use of GAI at a large online university in the United States. Guided by Expectancy-Value Theory, the multiple-methods case study used pre- and post-webinar surveys and qualitative analysis of open-ended responses.

Pre-webinar student responses showed strong interest in ethical and research-related applications but continued concern about academic integrity. Ethical uncertainty emerged as a central barrier to broader adoption, highlighting the need for clear guidance and continued training. As preliminary results from a pilot effort, these findings provide direction for larger future studies of GAI literacy interventions in doctoral education.

Conclusion

Preliminary findings from this pilot study indicate that targeted webinars may improve GAI literacy among doctoral faculty by increasing understanding, confidence, and ethical awareness. Continued concerns about misuse, academic integrity, and unclear institutional policy suggest that universities need clearer guidance and sustained professional development. Embedding ethical frameworks and practical skill building into future training may help ensure that GAI supports scholarship and instruction without compromising rigor. Further research with larger samples and stronger follow-up measures is needed to confirm effects and inform broader implementation.

Acknowledgments

GAI tools, including Copilot, ChatGPT, Perplexity, and Undermind, were used to support the development of this research proposal by assisting with idea generation, organization, and refinement.

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