CoChat
Sign Up
Cited by 23Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationArtificial Intelligence Applications

Engineering Educators' Perspectives on the Impact of Generative AI in Higher Education

Open access
Dewan, Umama, Hingle, Ashish, McDonald, Nora, Johri, Aditya·2025·2025 IEEE Global Engineering Education Conference (EDUCON)
DOI 10.1109/educon62633.2025.11016518
Open this paper in CoChat — free accountOpen PDF

Abstract

The introduction of generative artificial intelligence (GenAI) has been met with a mix of reactions by higher education institutions, ranging from consternation and resistance to whole-hearted acceptance. Previous work has looked at the discourse and policies adopted by universities across the U.S. as well as educators, along with the inclusion of GenAI-related content and topics in higher education. Building on previous research, this study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. Specifically, we surveyed 98 educators from engineering, computer science, and education who participated in a workshop on GenAI in Engineering Education to learn about their perspectives on using these tools for teaching and research. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it. Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably. We found that educators harbored mostly hopeful and positive views about the potential of GenAI. We also found that those who engaged more with their students on the topic of GenAI, both as communi-cators (those who spoke directly with their students) and as incorporators (those who included it in their syllabus), tend to be more positive about its contribution to learning, while also being more attuned to its potential abuses. These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology. Our work ultimately contributes to the evolving discourse on GenAI use, integration, and avoidance within educational settings. Through exploratory quantitative research, we have identified specific areas for further investigation.

AI snapshot

Written from the paper’s full text.

TL;DR

This survey of 98 engineering, computer science, and education educators found generally hopeful views of generative AI in higher education, with use varying considerably. Educators who communicated about GenAI with students or incorporated it into their syllabus tended to be more positive about its learning potential while also more attuned to potential abuses.

Key findings

  • Familiarity was high but not universal: 78% reported being somewhat or very familiar with GenAI, 17% somewhat unfamiliar, and 4% not at all familiar. Most held positive views: 77% agreed GenAI will transform higher education classrooms for good, 83% agreed it will transform the professional engineering workplace for good, 80% agreed it will transform course preparation and grading, and 70% believed it will significantly impact their research.
  • Reported use varied: 54% included GenAI policies in their syllabus, while 40% had incorporated GenAI into course assignments. For teaching, 65% used GenAI to prepare assignments, 55% for assessments, 53% for course content, and 34% for syllabus preparation. For research, 74% used it for writing, 54.5% for data analysis, 40.3% for research design, and 16.8% for data generation.
  • Perceived teaching challenges included privacy and awareness (30.9%), hindering learning (22.1%), difficulty with fair assessments (22.1%), veracity of GenAI output (11.7%), and lack of understanding (8.8%). For research, top concerns were veracity of output (23.4%) and plagiarism (18.8%), with privacy and awareness at 3%. Spontaneously reported risks/harms included hindering learning (39.7%), privacy and awareness (36.5%), veracity (31.7%), plagiarism (11.1%), and lack of understanding (7.9%).
  • GenAI Communicators, compared with non-communicators, felt more comfortable with available tools (50% vs 15%), were more likely to incorporate GenAI into the syllabus (72% vs 19%), regularly discussed GenAI ethics (70% vs 16%), and were more likely to have caught students using GenAI against stated syllabus policy (34% vs 4%). GenAI Incorporators, compared with non-incorporators, were more likely to communicate views to students (90% vs 49%), feel they had tools to handle policy violations (50% vs 29%), discuss ethics (92% vs 35%), and integrate GenAI into assignments (54% vs 24%).

Methods

  • The study used a Qualtrics survey with 13 questions covering demographics, perspectives on and use of GenAI in teaching and research, and perceived benefits and harms. It included multiple-choice, Likert-scale, and open-ended questions.
  • The survey was disseminated to 160 educators who joined a workshop on GenAI in engineering education, yielding 98 responses. Participants were 26% teaching faculty, 24% tenured professors, 18% tenure-track professors, 13% graduate students, and 4% post-doctoral candidates; 70% came from engineering, 7% from computer science/IT, and 12% from education.
  • Quantitative data were exported to Excel for descriptive statistics such as frequency distributions and percentages. Free-response questions were manually open-coded by one researcher using a codebook, with some risk/harm codes informed by existing literature. The analysis compared GenAI Communicators vs non-communicators and GenAI Incorporators in syllabus vs non-incorporators.

Limitations

  • The paper states that the study draws on a small, preliminary survey conducted before educators attended a GenAI workshop focused on engineering education, and that most respondents were engineering or computing faculty. This narrow scope limits generalizability to faculty in other fields.
  • The paper states that the study relies on self-reported perceptions of GenAI use in syllabi and assignments, though it notes no reason to believe educators misrepresented their use and argues self-report is appropriate for personal perceptions and contextual experiences.
  • Evident from the text: the sample was drawn from workshop attendees rather than a broader or random population, and the subgroup comparisons are exploratory, with the authors describing the work as exploratory quantitative research that identifies areas for further investigation.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1109/educon62633.2025.11016518

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 13 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    55% of participants used GenAI for preparing assessments.

    “while 55% of the participants used it for preparing assessments.”
    Quote in the paperNot a substantial claim
  • Findingown data

    74% of participants report using GenAI for writing in research.

    “GenAI is being used in various aspects of research, with writing being the most prominent area (74%).”
    Quote in the paperNot a substantial claim
  • Findingown data

    70% of respondents believed GenAI will significantly impact their research.

    “70% respondents believed it will significantly impact their research.”
    Quote in the paperNot a substantial claim
  • Findingown data

    80% of respondents acknowledged that GenAI will transform their course preparation and grading processes.

    “80% respondents acknowledged that GenAI will transform their course preparation and grading processes”
    Quote in the paperSubstantial claim
  • Findingown data

    40% of participants are using GenAI for research design.

    “and 40% are using it for research design.”
    Quote in the paperNot a substantial claim
  • May combine two claims. 30
  • Classified as probably not a substantial claim. 25

  • May combine two claims.

    Findingown data

    Among surveyed educators, 78% reported being somewhat or very familiar with GenAI, 17% somewhat unfamiliar, and 4% not at all familiar.

    “Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably.”
    • Also stated in: § IV. FINDINGS · plain
      “Of our participants, 78% reported being somewhat or very familiar with GenAI.In contrast, 17% indicated that they were somewhat unfamiliar, and 4% stated that they were not at all familiar with GenAI (Table IV).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Most participants held a positive view of GenAI's impact on higher education and their professional practices.

    “Most participants held a positive view of GenAI's impact on higher education and their professional practices.”
    • Also stated in: § Abstract · plain
      “We found that educators harbored mostly hopeful and positive views about the potential of GenAI.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Veracity of GenAI output was the most significant concern in research, with 23% of participants expressing doubts about accuracy and reliability.

    “The veracity of GenAI output stands out as the most significant concern, with 23% participants expressing doubts about the accuracy and reliability of AIgenerated content in research contexts.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    20% of participants reported using AI for other unspecified research tasks.

    “and other unspecified research tasks (20%).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    55% of Incorporators somewhat agree that GenAI will transform higher education classrooms for good, while a greater percentage of Non-Incorporators (64%) somewhat agree with this statement.

    “For example, while 55% of Incorporators somewhat agree that GenAI will transform higher education classrooms for good, a greater percentage of Non-Incorporators (64%) somewhat agree with this statement.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    54% of Incorporators have integrated GenAI into their course assignments to some extent, while only 24% of Non-Incorporators have done so.

    “In addition, while 54% of Incorporators educators have integrated GenAI into their course assignments to some extent, only 24% of Non-Incorporators educators have done so.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Among the potential risks or harms foreseen with GenAI, hindering learning was the most frequently cited (39.7%), followed by privacy and awareness (36.5%), veracity of GenAI output (31.7%), plagiarism (11.1%) and lack of understanding (7.9%).

    “Hinders learning 39.7% Privacy and awareness 36.5% Veracity of GenAI output 31.7%Plagiarism 11.1% Lack of understanding 7.9%”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    For the activity 'Learning Course Concepts', 50% of communicators see a positive impact of GenAI, while only 28% of non-communicators share this view.

    “For example, in “Learning Course Concepts,” 50% of communicators see a positive impact, while only 28% of non-communicators share this view.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    85% of Non-Incorporators agree that GenAI will impact curriculum design, with a higher proportion (54%) strongly agreeing.

    “In contrast, 85% of Non-Incorporators agree that GenAI will impact curriculum design, with a higher proportion (54%) strongly agreeing.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning

    While acknowledging some benefits of GenAI in specific areas like problem-solving and learning concepts, Incorporators are more cautious about its broader educational impact.

    “Interestingly, while acknowledging some benefits of GenAI in specific areas like problem-solving and learning concepts, Incorporators are more cautious about its broader educational impact.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Both Incorporators and Non-Incorporators generally agree on the transformative potential of GenAI in higher education, but Non-Incorporators display greater confidence in its positive impact.

    “Although both Incorporators and Non-Incorporators generally agree on the transformative potential of GenAI in higher education, Non-Incorporators surprisingly display greater confidence in its positive impact.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    There is a positive relationship between engagement with GenAI and both enthusiasm for and realistic perceptions of its use.

    “Our research demonstrates a positive relationship between engagement with GenAI and both enthusiasm for and realistic perceptions of its use.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    Non-Incorporators exhibit more variability in their responses, with stronger positive perceptions in areas like critical thinking and code-related tasks but also a higher tendency to see GenAI as irrelevant to their teaching practices.

    “Non-Incorporators, on the other hand, exhibit more variability in their responses, with stronger positive perceptions in areas like critical thinking and code-related tasks but also a higher tendency to see GenAI as irrelevant to their teaching practices.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Instructors who engage with and incorporate GenAI have a slightly more positive outlook on GenAI across various academic activities, even if all instructors seem generally positive about the technology.

    “These instructors also have a slightly more positive outlook on GenAI across various academic activities, even if all instructors seem generally positive about the technology (table XIII and XVI).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    These figures highlight a widespread recognition of GenAI's potential across academic and professional settings, with particular emphasis on transforming educational and research workflows.

    “These figures highlight a widespread recognition of GenAI's potential across both academic and professional settings, with a particular emphasis on its role in transforming educational and research workflows (Figure 1).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Generative AI technologies can produce responses and code so similar to human outputs that they may easily be mistaken for them, raising concerns about academic integrity, originality of student work, and the evolving role of educators.

    “Generative AI technologies can produce responses and code so similar to human outputs that they may easily be mistaken for them, raising concerns about their implications for academic integrity, the originality of student work, and the evolving role of educators in guiding learning [1]- [3].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [1]- [3]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Ali et al. identified exemplary practices among R1 Engineering and Computing educators, including presenting authentic experiences and taking a transparent and thorough approach to articulating GenAI policies.

    “Ali et al. [32] identified exemplary practices among R1 Engineering and Computing educators, including presenting authentic experiences and taking a transparent and thorough approach to articulating GenAI policies.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationown data

    Integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology.

    “These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Potential for plagiarism is a key issue with GenAI in education.

    “Key issues include the potential for plagiarism, questions about the trustworthiness of AI-generated content, and concerns that reliance on these tools might weaken students' critical thinking and problemsolving abilities [5], [6].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [5], [6]

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Research priorities are primarily focused on ensuring the integrity and quality of AI-generated outputs, with comparatively less emphasis on broader impacts such as learning outcomes and content depth.

    “This suggests that research priorities are primarily focused on ensuring the integrity and quality of AI-generated outputs, with comparatively less emphasis on broader impacts, such as learning outcomes and content depth.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Prior work has shown that adoption within higher education is a non-linear process, with marked differences in use by early adopters, likely adopters, and non-adopters.

    “Prior work has shown that, as with any technology, the adoption within higher education is a non-linear process, with marked differences in use by early adopters, likely adopters, and non-adopters [30].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [30] source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Educators struggle with the unrealized potential of GenAI, often reporting that they use it primarily for superficial tasks, while lamenting the lack of institutional support for its effective and ethical use.

    “It is also evident that educators struggle with the unrealized potential of GenAI, often reporting that they use it primarily for superficial tasks, while lamenting the lack of institutional support for its effective and ethical use [14].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [14] source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Non-Incorporators' stronger agreement might reflect a theoretical appreciation of GenAI's potential, without the practical challenges that Incorporators might experience in integrating these technologies into their courses.

    “Their stronger agreement might reflect a theoretical appreciation of GenAI's potential, without the practical challenges that Incorporators might experience in integrating these technologies into their courses.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationreasoning

    Higher disagreement levels in the non-communicators group, particularly regarding curriculum design and research impact, could indicate uncertainty or lack of exposure to GenAI's potential.

    “Higher disagreement levels in the non-communicators group, particularly regarding curriculum design and research impact, could indicate uncertainty or lack of exposure to GenAI's”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Questions about the trustworthiness of AI-generated content are a key issue with GenAI in education.

    “Key issues include the potential for plagiarism, questions about the trustworthiness of AI-generated content, and concerns that reliance on these tools might weaken students' critical thinking and problemsolving abilities [5], [6].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [5], [6]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Concerns that reliance on GenAI might weaken students' critical thinking and problem-solving abilities are a key issue.

    “Key issues include the potential for plagiarism, questions about the trustworthiness of AI-generated content, and concerns that reliance on these tools might weaken students' critical thinking and problemsolving abilities [5], [6].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [5], [6]

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    Non-communicators may have more reservations about GenAI or lack confidence in their understanding or application of it.

    “It might also suggest that non-communicators have more reservations or lack confidence in their understanding or application of GenAI.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Kaplan-Rakowski et al. found generally positive experiences with GenAI tools and noted more frequent use corresponded with increasingly positive perspectives.

    “In their survey of teachers who had used GenAI at least once, Kaplan-Rakowski et al. [28] found generally positive experiences with GenAI tools and noted more frequent use corresponded with increasingly positive perspectives.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [28] Kaplan-Rakowski, 2023, Gener-ative ai and teachers’ perspectives on its implementation in education, Journal of Interactive Learning Research

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study draws on data from a small, preliminary survey conducted on educators before they attended a workshop on the use of GenAI for teaching and research.

    “A limitation of this study is that it draws on data from a small, preliminary survey conducted on educators before they attended a workshop on the use of GenAI for teaching and research.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Instructors who choose not to engage with GenAI in the classroom may also be consciously avoiding it due to concerns about its legitimacy and potential erosion of learning.

    “Instructors who choose not to engage with GenAI in the classroom may also be consciously avoiding it due to concerns about its legitimacy and potential erosion of learning.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Study design and conductreasoning

    Self-reported data is appropriate for understanding personal perceptions and contextual experiences, which are central to this study's objectives.

    “Self-reported data is indeed appropriate for understanding personal perceptions and contextual experiences, which are central to this study's objectives.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Differing levels of familiarity with and acceptance of GenAI exist among educators, indicating a need for further exploration and support to address variations in perception and usage.

    “This indicates differing levels of familiarity and acceptance of GenAI, highlighting the need for further exploration and support to address these variations in perception and usage.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Reference [32] provides guidance on best practices for incorporating GenAI into syllabi.

    “[32] provides guidance on best practices for incorporating GenAI into syllabi.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning

    Instructors' concerns about plagiarism may intensify when plagiarism impacts the assessment of their own work.

    “It may be that their concerns intensify when plagiarism impacts the assessment of their work.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    There may be a gap in providing instructors with opportunities for exposure and instructional support, such as tutorials and case studies, which could further enhance their comfort and effectiveness in using GenAI in the classroom.

    “However, there may very well be a gap in providing instructors with opportunities for exposure and instructional support, such as tutorials and case studies, which could further enhance their comfort and effectiveness in using GenAI in the classroom.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Engagement with GenAI (communicating about it and incorporating it into the syllabus) causally produces the more positive and realistic perceptions observed, rather than the reverse or a common cause.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationown data

    The findings lay the groundwork for further exploration of GenAI's implications, particularly in the classroom, highlighting the need for ongoing support and resources to promote meaningful adoption and ethical engagement in instruction and research.

    “Our findings lay the groundwork for further exploration of GenAI's implications, particularly in the classroom, highlighting the need for ongoing support and resources to promote meaningful adoption and ethical engagement in both instruction and research.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Engagement with GenAI (communicating about it and incorporating it into the syllabus) causally produces the more positive and realistic perceptions observed, rather than the reverse or a common cause.

  • Classified as probably not a substantial claim.

    Interpretationown data

    By analyzing educators' responses through the lens of communication and integration practices, the authors identified how varying attitudes and levels of use shape teaching methods and perceptions.

    “By analyzing their responses through the lens of communication and integration practices, we identified how varying attitudes and levels of use shape teaching methods and perceptions.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Engagement with GenAI (communicating about it and incorporating it into the syllabus) causally produces the more positive and realistic perceptions observed, rather than the reverse or a common cause.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationnone given

    Communicators may excel at fostering dialogue and shaping student attitudes, while incorporators are more focused on the structural integration of GenAI within curricula.

    “For example, while communicators may excel at fostering dialogue and shaping student attitudes, incorporators are more focused on the structural integration of GenAI within curricula.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Study design and conductnone given

    The authors have no reason to believe that educators misrepresented their GenAI use.

    “we have no reason to believe that educators misrepresented their use.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    A key challenge for instructors is the potential for GenAI to hinder genuine learning and compromise fair assessment practices.

    “Our analysis highlights that a key challenge for instructors is the potential for GenAI to hinder genuine learning and compromise fair assessment practices.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Educators often perceive GenAI as a threat to the originality of students' work, associating its use with academic misconduct such as plagiarism.

    “Luo [36] emphasizes that educators often perceive GenAI as a threat to the originality of students' work, associating its use with academic misconduct, such as plagiarism.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationown data

    The findings suggest that proactive engagement with GenAI is a promising approach.

    “Although our study was not explicitly designed to identify such best practices, the findings suggest that proactive engagement with GenAI is a promising approach.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Engagement with GenAI (communicating about it and incorporating it into the syllabus) causally produces the more positive and realistic perceptions observed, rather than the reverse or a common cause.

  • May combine two claims.

    Study design and conductnone given

    The workshop focused specifically on engineering education, and the majority of the respondents were faculty members in engineering or closely related to computing disciplines.

    “The workshop focused specifically on engineering education, and the majority of the respondents were faculty members in engineering or closely related to computing disciplines.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The veracity of GenAI content and its impact on learning outcomes and fair assessments have been widely discussed in the literature.

    “Issues such as the veracity of GenAI content have been widely discussed in the literature, as well as its impact on learning outcomes [25], [34], [35] and fair assessments [26], [27].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [25], [34], [35]

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Many educators who incorporate GenAI into their courses also tend to communicate their perspectives openly, but there are important nuances.

    “The overlap between these groups illustrates that many educators who incorporate GenAI into their courses also tend to communicate their perspectives openly, but there are important nuances.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    There may be reasons why instructors are resistant to advice on incorporating GenAI.

    “Our analysis suggests that there may be reasons why instructors are resistant to this advice, as we discuss in the next section.”
    Quote in the paperNot a substantial claim
46 of 46

  • The paper relies on this without support.

    No prior research has looked at the relationship between instructors' experiences through the use of GenAI, their communications with students about GenAI, and their perspectives on its influence on education.

    “So far as we know, no research has looked at the relationship between instructors' experiences through the use of GenAI, their communications with students about GenAI, and their perspectives on its influence on education.”

  • The paper relies on this without support.

    The survey respondents are representative of engineering educators in higher education.

    “We disseminated this survey to 160 educators who joined a workshop on GenAI in engineering education, yielding a total of 98 responses.”

  • The paper relies on this without support.

    Engagement with GenAI (communicating about it and incorporating it into the syllabus) causally produces the more positive and realistic perceptions observed, rather than the reverse or a common cause.

    “We recommend that educators engage with GenAI, at a minimum, to develop greater awareness or cultivate a more nuanced understanding of its potential applications.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Institutions have shifted toward alternative assessment methods, such as oral exams (viva voce), real-world reflective tasks, scaffolded assignments, reflective journals, Bloom's Taxonomy higher-order thinking questions, and interactive problem-solving tasks, which require direct student engagement and cannot be easily outsourced to AI.

    “To achieve this, institutions have shifted toward alternative assessment methods, such as oral exams (viva voce), real-world reflective tasks, scaffolded assignments, reflective journals, use of Bloom's Taxonomy higher-order thinking questions, and interactive problemsolving tasks, which require direct student engagement and cannot be easily outsourced to AI [ 56, 65, 67, 70, 73, 79, 89, 94, 97-99”

    From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [73]

    Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Strong agreement on curriculum design is misreported

    Minor

    The text says 54% of Non-Incorporators strongly agree that GenAI will influence curriculum design. Table XV lists 54% as somewhat agreeing and 31% as strongly agreeing.

  • Code-related impact is less positive among Non-Incorporators

    Minor

    The text describes Non-Incorporators as having stronger positive perceptions of code-related tasks, but Table XVI shows a positive impact reported by 40% of Non-Incorporators versus 55% of Incorporators.

2 of 2

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. Mollman, 2022, Chatgpt gained 1 million users in under a week. here’s why the ai chatbot is primed to disrupt search as we know it
  2. Elsen-Rooney, 2023, Nyc education department blocks chatgpt on school devices, networks
  3. Boehm, 2024, Openai launches first university partnership with arizona state
  4. Khandkar, 2009, Open coding, University of Calgary, 23, 2009

Cited by

Sorted by citations

Related

Authors
Dewan, U., Hingle, A., McDonald, N., & Johri, A.
Affiliations
George Mason University (US)
ORCID
Venue
2025 IEEE Global Engineering Education Conference (EDUCON)
Publisher
IEEE
Published
2025-04-22
Pages
1-10
DOI
10.1109/educon62633.2025.11016518
OpenAlex
W4410986634
Semantic Scholar
847d6c532445d762aace63a474c3463393247304
Cited by
16
References
32
Citations by year
2026: 11 · 2025: 5
FWCI
18.30
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Global Engineering Education Conference (EDUCON) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
generative AI, engineering education, educator perspectives, AI in teaching, artificial intelligence in research, instructor-student interaction, AI harms, AI adoption in higher education, computer science education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review