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Cited by 9Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationBiomedical and Engineering Education

Integrating Generative Artificial Intelligence Tools and Competencies in Biomedical Engineering Education

Open access
Khojah, Reem, Werth, Alexandra, Broadhead, Kelly W., Dobrucki, Lawrence W., Geiger, Chris, Rubenstein, David A.·2025·Biomedical Engineering Education
DOI 10.1007/s43683-025-00175-9
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AI snapshot

Written from the paper’s full text.

TL;DR

This summit report synthesizes discussions from the Fifth Biomedical Engineering Education Summit on integrating generative AI into biomedical engineering education, both as a curriculum competency and as a teaching/learning tool. It offers recommendations for curriculum design, ethics, assessment, policy, and institutional support while highlighting integrity, bias, equity, and assessment gaps.

Key findings

  • Survey data from 102 BME educators across 40 institutions showed that most institutions were still drafting or had not started GenAI misconduct policies; about one-third provided access to tools such as ChatGPT, Copilot, or Gemini; most expected students to disclose AI use, but few monitored or verified submissions; roughly two-thirds reported grade penalties for unacknowledged use.
  • The paper recommends splitting AI competencies into “effective” skills (mathematical foundations, programming, machine learning techniques, model evaluation and bias awareness) and “enduring” skills (open science, ethical decision-making, interdisciplinary collaboration, adaptability and lifelong learning).
  • Curricular integration options include embedding AI into existing courses, specialized AI/bioinformatics tracks, short modules, co-teaching, and industry partnerships; relying only on electives risks uneven student preparation.
  • GenAI can personalize learning, assist with grading and feedback, lower software learning curves, and support AI-resilient assignments such as critiquing AI outputs, error detection, custom problem generation, visual interpretation, and open exploration, but reliability, bias, privacy, copyright, over-reliance, and detection limits require human oversight. Cited trends include a projected 38.5% compound annual growth rate for AI in healthcare from 2024 to 2030, 75.2% of 2023 AI-related healthcare publications using image data, and 118 ABET-accredited BME programs.

Methods

  • The report synthesizes pre-summit survey responses from invited BME faculty representatives, two days of summit discussions and breakout sessions with approximately 400 participants from about 140 invited institutions, and post-summit literature review plus informal community feedback.
  • It is organized around a question-response format covering two themes: embedding GenAI competencies into BME curricula to match industry needs, and using GenAI as an educational tool to enhance or challenge teaching and learning.
  • It includes adapted cases from prior work and survey results from 102 educators across 40 institutions; no primary experimental or controlled study is reported.

Limitations

  • Stated in the paper: no comprehensive systematic review of AI education in BME yet exists; research-based assessment tools for AI competencies in engineering are lacking; more work is needed to reach consensus on BME industry needs; AI content detection remains underdeveloped and reliance on a single detector is concerning.
  • Stated/evident: recommendations draw on summit discussions, survey responses, anecdotal evidence, and informal feedback rather than controlled outcome data, and many claims about GenAI’s educational impact are forward-looking or case-based.
  • Evident from the text: survey reporting is limited to figure-level summaries without response-rate or psychometric validation details, and equity, infrastructure, and bias concerns are discussed but not empirically tested.

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

Integrity signals

Registry

Crossref · 10.1007/s43683-025-00175-9

Cites

39 of 39 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

25 of 68 cited sources checked · 16 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    AI competencies have not been determined in medical education and health informatics.

    “Additionally, current medical education and health informatics accreditation standards do not require AI training, and AI competencies have not been determined.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [5] Sapci A, Sapci H. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020;6(1):19285. https://doi.org/10.2196/19285. source
  • Premisecitation

    A 2020 systematic review by Sapci and Sapci describes approaches for adding AI to curricula, especially in medical and health informatics teaching.

    “A systematic review by Sapci and Sapci (2020) [5] highlights strategies to integrate AI into curricula, particularly in medical and health informatics education.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [5] Sapci A, Sapci H. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020;6(1):19285. https://doi.org/10.2196/19285. source
  • Premisecitation

    The review concluded that traditional medical and health informatics programs still do not include AI and machine learning instruction.

    “The review found that although there are several recommendations on the integration of AI into medical and health informatics curricula and some academic institutions implemented experimental training programs, AI and ML education are not a part of traditional medical and health informatics curricula yet.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [5] Sapci A, Sapci H. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020;6(1):19285. https://doi.org/10.2196/19285. source
  • Premisecitation

    AI-enhanced study assistants offer real-time, on-demand support, enabling continuous and personalized learning outside the classroom.

    “AI-enhanced study assistants offer real-time, on-demand support, enabling continuous and personalized learning outside the classroom [27].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [27] Ai study partner. Development of an llm and genai-enhanced study assistant tool. Int J Sci Res Eng Manag. 2024;8(3):1–5. https://doi.org/10.55041/ijsrem29626. source

      The Abstract says the tool offers personalized support that responds to individual needs in real time, and Results & Discussion describes students quickly obtaining feedback. The copy also discusses independent study, but it does not establish continuous availability or specifically describe learning outside the classroom. No page numbers are available in the indexed passages. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • Premisecitation

    In a graduate-level computational bioengineering course at Vanderbilt University, instructors designed homework assignments to leverage LLM capabilities while fostering critical thinking, computational skills, and deeper understanding of numerical and statistical methods.

    “In a graduate-level computational bioengineering course at Vanderbilt University, the instructors designed innovative homework assignments to leverage LLM capabilities while fostering critical thinking, computational skills, and a deeper understanding of numerical and statistical methods.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [36] King MR, Abdulrahman AM, Petrovic MI, Poley PL, Hall SP, Kulapatana S, Lamantia ZE. Incorporation of chatgpt and other large language models into a graduate level computational bioengineering course. Cell Mol Bioeng. 2024;17(1):1–6. source

      Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

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

    Premisecitation

    Sapci and Sapci reported that the reviewed articles fell into two groups: using AI tools to improve medical teaching and teaching AI as a new skill.

    “Sapci & Sapci found that the articles reviewed could be broken into two categories: (1) AI tools to enhance medical education and (2) teaching AI as a new competency.”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [5] Sapci A, Sapci H. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020;6(1):19285. https://doi.org/10.2196/19285. source

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

    Premisecitation

    The review's findings stress that students need hands-on AI abilities, including spotting and fixing algorithm mistakes, working across disciplines, and improving digital literacy.

    “The findings emphasize the need to equip students with practical AI skills, such as assessing and correcting algorithmic errors, fostering interdisciplinary collaboration, and enhancing digital literacy.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [5] Sapci A, Sapci H. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020;6(1):19285. https://doi.org/10.2196/19285. source

  • May combine two claims.

    Premisecitation

    Accreditation requirements for medical education and health informatics currently do not mandate AI instruction.

    “Additionally, current medical education and health informatics accreditation standards do not require AI training, and AI competencies have not been determined.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [5] Sapci A, Sapci H. Artificial intelligence education and tools for medical and health informatics students: systematic review. JMIR Med Educ. 2020;6(1):19285. https://doi.org/10.2196/19285. source

  • May combine two claims.

    Premisecitation

    AI tools can assist in automating routine advising tasks, identifying at-risk students, recommending courses, and providing timely responses to student inquiries.

    “AI tools can assist in automating routine advising tasks, identifying at-risk students, recommending courses, and providing timely responses to student inquiries.”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [37] Ai-based academic advising framework: a knowledge management perspective. Int J Adv Comput Sci Appl 2022: https://doi.org/10.14569/ijacsa.2022.0130823 source
    • cites [38] Akiba D. Ai-supported academic advising: exploring chatgpt’s current state and future potential toward student empowerment. Educ Sci. 2023;13(9):885. https://doi.org/10.3390/educsci13090885. source

      The Introduction says AI-powered tools such as ChatGPT are available around the clock, and the Discussion describes students obtaining answers immediately, supporting timely responses to inquiries. The paper does not establish that these tools identify at-risk students or automate routine advising tasks. In Materials and Methods, the authors excluded course-selection questions because ChatGPT lacked access to individual student information, so the course-recommendation part is not supported. Page numbers are not available in our copy. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

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    Premisecitation

    Building proprietary generative AI systems allows institutions to manage sensitive data securely, ensuring compliance with FERPA, while reducing risks associated with third-party platforms.

    “Building proprietary generative AI systems allows institutions to manage sensitive data securely, ensuring compliance with regulations such as FERPA, while reducing the risks associated with third-party platforms [41,42].”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [41] The use of chatgpt in teaching and learning: a systematic review. Front Educ. 2023. https://doi.org/10.3389/feduc.2023.1259702. source

      Not checked in the free run.

    • cites [42] Leveraging generative ai for sustainable academic advising: enhancing educational practices. Sustainability. 2023;16(17):7829. https://doi.org/10.3390/su16177829. source

      The Data availability section says human-generated data are restricted for privacy reasons, but does not link that restriction to a proprietary generative AI system. The Discussion describes using ChatGPT through the OpenAI API; neither it nor the other passages found addresses FERPA compliance or reduced risks from third-party platforms (searched: FERPA, proprietary systems, data privacy, third-party platforms). (LLM, conf=0.74)

      Experimental — accuracy not yet measured

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

    Premisecitation

    In a graduate-level computational bioengineering course, students were provided with ground rules for using generative AI.

    “In a graduate-level computational bioengineering course, which required students to interact with ChatGPT for course assignments, students were provided with “ground rules” for using GenAI.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites King et al., 2024 source
    • cites [36] King MR, Abdulrahman AM, Petrovic MI, Poley PL, Hall SP, Kulapatana S, Lamantia ZE. Incorporation of chatgpt and other large language models into a graduate level computational bioengineering course. Cell Mol Bioeng. 2024;17(1):1–6. source

  • Classified as probably not a substantial claim.

    Premisecitation

    Research underscores the need for clear guidelines to govern AI use in academic advising and mentorship.

    “Research underscores the need for clear guidelines to govern AI use in academic advising and mentorship [39,40].”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [39] Ethical issues of ai in education. Front Public Health. 2022;10:1118116. https://doi.org/10.3389/fpubh.2022.1118116. source

      The Results section says ethical principles and guidelines should govern activities throughout the AI system life cycle, including implementation and oversight. That supports the need for guidelines generally, but the paper does not specifically address AI use in academic advising or mentorship (no page numbers are available in our copy). (LLM, conf=0.85)

      Experimental — accuracy not yet measured
    • cites [40] Generative ai in education–balancing innovation with governance. Nature 2024 https://doi.org/10.1038/d41586-024-03070-5 source

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

    Premisecitation

    Literature emphasizes that without appropriate oversight, AI could contribute to issues such as plagiarism and over-reliance on AI-generated content for academic work.

    “Literature emphasizes that without appropriate oversight, AI could contribute to issues such as plagiarism and over-reliance on AI-generated content for academic work [39,43].”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [39] Ethical issues of ai in education. Front Public Health. 2022;10:1118116. https://doi.org/10.3389/fpubh.2022.1118116. source

      The Introduction discusses risks to privacy and student autonomy, and the Results mention academic integrity in the context of algorithmic proctoring. Neither passage addresses plagiarism or students’ over-reliance on AI-generated academic work. Searches for those risks and related terms found no passage establishing the attributed claim. (LLM, conf=0.76)

      Experimental — accuracy not yet measured
    • cites [43] Will chatgpt get you caught? rethinking of plagiarism detection. PsyArXiv 2023 https://doi.org/10.35542/osf.io/fnh48 source

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

    Premisecitation

    Reliability of AI-generated content and potential biases in information processing are challenges that need careful consideration.

    “Issues such as the reliability of AIgenerated content and the potential for biases in information processing must be carefully considered [28,29].”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [28] Gökoğlu S. Challenges and Limitations of Generative AI in Education, (2024). https://doi.org/10.4018/979-8-3693-1351-0.ch008 source
    • cites [29] McCabe N. A teacher’s guide to creating materials that support efficient learning: from scientific background to ai-based content creation 2024 https://doi.org/10.31234/osf.io/qz64y source

  • May combine two claims.

    Premisecitation

    GenAI has enabled the creation of personalized, adaptive learning materials that cater to individual student needs, allowing instructors to provide tailored content for diverse learning styles and backgrounds.

    “Presently, GenAI has enabled the creation of personalized, adaptive learning materials that cater to individual student needs, allowing instructors to provide tailored content for diverse learning styles and backgrounds [23][24][25][26].”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites [23] Zhang X, Zhang P, Shen Y, Liu M, Wang Q, Gašević D, Fan Y. A systematic literature review of empirical research on applying generative artificial intelligence in education. Front Dig Edu. 2024;1(3):223–45. source
    • cites [24] Yan L, Greiff S, Teuber Z, Gašević D. Promises and challenges of generative artificial intelligence for human learning. Nat Human Behav. 2024;8(10):1839–50. source
    • cites [25] Chan K, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med Educ. 2019. https://doi.org/10.2196/13930. source

      The Introduction says AI can help students receive specialized help and improve personalized, adaptive teaching; the Abstract > Conclusions also reports individualized feedback. But this 2019 review does not establish the claim about GenAI creating learning materials tailored by instructors to students’ learning styles and backgrounds. The indexed passages provide section labels but no page numbers. (LLM, conf=0.88)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    Rubenstein and Kim (2024) integrated ChatGPT into a graduate cellular and molecular bioengineering course where students solved complex problems, critiqued AI solutions, and incorporated AI insights into exams.

    “In Case 3.4.2,Rubenstein & Kim (2024) [53] integrated ChatGPT into a graduate cellular and molecular bioengineering course, where students solved complex problems, critiqued AI solutions, and incorporated AI insights into exams.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites Rubenstein & Kim (2024) source
    • cites [53] Rubenstein DA, Kim C. Perspectives on incorporating a large language model into a cellular and molecular bioengineering graduate course. Cellular and Molecular Bioengineering, 2024;1–4 source

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

    Findingnone given

    Summit participants stressed the importance of clear policies to uphold academic integrity.

    “Additionally, they stressed the importance of developing clear policies to uphold academic integrity.”
    Quote in the paperNot a substantial claim

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

    Findingreasoning

    Summit participants emphasized AI's potential to enhance educational technologies.

    “Participants emphasized the need for intentional instruction on AI as a core skill, as well as its potential to enhance educational technologies.”
    Quote in the paperNot a substantial claim

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

    Findingreasoning

    The summit discussions indicated that AI might reshape BME teaching by enhancing programming, data analysis, and visualization, while also raising concerns about fair access and privacy.

    “The discussions highlighted AI's potential to transform BME education by improving coding, data analysis, and visualization capabilities, while also raising concerns about equitable access and privacy.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Findingown data

    Summit participants emphasized that AI should be intentionally taught as a core skill.

    “Participants emphasized the need for intentional instruction on AI as a core skill, as well as its potential to enhance educational technologies.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingnone given

    A comprehensive review of AI teaching in BME has not yet been conducted, whereas health informatics and medical education have such reviews.

    “Unlike health informatics and medical education, where systematic reviews have been conducted to evaluate the state of AI education, no such comprehensive review has yet been undertaken in BME education.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The modules lead students to find relevant standards, write compliance protocols, suggest changes to existing standards, and draft new ones.

    “These modules guide students through searching and identifying relevant standards, writing protocols to verify compliance, proposing revisions to existing standards, and drafting new ones.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [11] Browne MG, Felder AE, Defante AP. Work-in-progress: Development of a domain-agnostic standards curriculum in partnership with a medical device manufacturer. In: 2024 ASEE Annual Conference & Exposition 2024 source

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

    Premisecitation

    The last module asks students to write parts of a standard for using generative AI in biomedical engineering assignments.

    “In the final module, students are tasked with outlining and drafting sections of a standard governing the use of generative AI in completing biomedical engineering coursework.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [11] Browne MG, Felder AE, Defante AP. Work-in-progress: Development of a domain-agnostic standards curriculum in partnership with a medical device manufacturer. In: 2024 ASEE Annual Conference & Exposition 2024 source

  • May combine two claims.

    Premisecitation

    Situated cognition and problem-based learning are reported to raise student motivation and deepen learning.

    “These approaches have been shown to enhance student motivation and promote deeper learning [4].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [4] Hung D. Situated cognition and problem-based learning: Implications for learning and instruction with technology. J Interact Learn Res. 2002;13(4):393–414.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    An IEEE 2024 position paper, Grand Challenges at the Interface of Engineering and Medicine, points to AI and machine learning as promising for major health problems, including personalized physiology avatars, brain-interface systems, and immune system engineering.

    “This aligns with the vision outlined in IEEE's 2024 position paper, Grand Challenges at the Interface of Engineering and Medicine, which highlights the transformative potential of AI and machine learning in addressing major healthcare challenges, such as personalized physiology avatars, brain-interface systems, and immune system engineering [8].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [8] Medicine IE, Society B. Grand challenges at the interface of engineering and medicine. IEEE Open Journal of Engineering in Medicine and Biology (2024). Published by IEEE EMBS with contributions from 50 researchers representing 34 institutions

      Not checked in the free run.

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

    Interpretationreasoning

    Because GenAI technologies advance rapidly and may become obsolete within a few years, it is difficult to predict which skills will remain most relevant.

    “The rapid pace of GenAI development presents a significant challenge in curriculum design.Technologies that are cutting-edge today may become obsolete within a few years, making it difficult to predict which skills will be most relevant.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Browne and colleagues also found that teams updated the standard to close those gaps, adding ways to continually assess AI reliability and ethical compliance.

    “In addition, they found that teams revised the standard to address these gaps, incorporating mechanisms for ongoing evaluation of AI reliability and ethical compliance.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [11] Browne MG, Felder AE, Defante AP. Work-in-progress: Development of a domain-agnostic standards curriculum in partnership with a medical device manufacturer. In: 2024 ASEE Annual Conference & Exposition 2024 source

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

    Premisecitation

    Backwards curricular design aligns teaching practices with desired outcomes and ensures coherence among outcomes, assessments, and learning activities.

    “A critical framework for aligning teaching practices with desired outcomes is backwards curricular design [16].”
    • Also stated in: § How can We Assess Our Effectiveness of Teaching New AI Competencies to BME Students? · plain
      “This method ensures coherence between outcomes, assessments, and learning activities, fostering both student success and curriculum effectiveness.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [16] Wiggins G. Understanding by design. Association for Supervision and Curriculum Development 2005

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Interpretationreasoning

    Metrics like participation rates and qualitative feedback from diverse student populations can highlight areas for improvement, ensuring inclusive access and outcomes.

    “Metrics like participation rates and qualitative feedback from diverse student populations can highlight areas for improvement, ensuring inclusive access and outcomes.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Programs that leverage GenAI for personalized learning could improve students' ability to solve complex biomedical problems, aligning with ABET's requirement for using modern engineering tools and methodologies.

    “For instance, programs that leverage GenAI for personalized learning could improve students' ability to solve complex biomedical problems, which aligns with ABET's requirement for using modern engineering tools and methodologies [48].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [48] Criteria for Accrediting Engineering Programs (ABET). https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-programs-2023-2024/#GC3

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Premisecitation

    Tasdemir and Gazo (2020) argue that institutions and departments at all levels can contribute to curriculum development if they recognize the urgency and complexity of sustainability challenges.

    “Tasdemir & Gazo (2020) argue that institutions and departments at all levels can play a role in these curriculum development efforts, provided they recognize the urgency and complexity of challenges posed by sustainability [18].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Tasdemir C, Gazo R. Integrating sustainability into higher education curriculum through a transdisciplinary perspective. J Clean Prod. 2020;265: 121759. source

  • May combine two claims. The quote only partly states this claim.

    Premisecitation

    Pre- and post-course surveys in the Purdue sustainability course showed a significant increase in students' awareness of sustainability concepts and confidence in applying management tools to real-world challenges.

    “post-course surveys revealed a significant increase in students' awareness of sustainability concepts and their confidence in applying management tools to real-world challenges.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Tasdemir C, Gazo R. Integrating sustainability into higher education curriculum through a transdisciplinary perspective. J Clean Prod. 2020;265: 121759. source

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

    Premisecitation

    The sustainability-focused transdisciplinary course at Purdue successfully enhanced students' understanding of sustainability and modern management techniques.

    “Tasdemir and Gazo [18] reported that the course successfully enhanced students' understanding of sustainability and modern management techniques.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Tasdemir C, Gazo R. Integrating sustainability into higher education curriculum through a transdisciplinary perspective. J Clean Prod. 2020;265: 121759. source

  • May combine two claims.

    Interpretationcitation

    Assessments that target higher-order thinking and problem-solving encourage critical thinking and creativity, making it more difficult for students to rely on simple automation to complete tasks.

    “These assessments encourage critical thinking and creativity, making it more difficult for students to rely on simple automation to complete tasks.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [31] Bloom BS. Taxonomy of educational objectives, handbook: the cognitive domain. New York: David McKay; 1956.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Interpretationnone given

    Faculty surveys, course material reviews, and peer feedback can reveal whether instructors are using GenAI to create more interactive, project-based learning experiences or streamline administrative tasks to focus on student engagement.

    “Faculty surveys, course material reviews, and peer feedback can reveal whether instructors are using GenAI to create more interactive, project-based learning experiences or streamline administrative tasks to focus on student engagement.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    Tracking shifts in teaching practices helps demonstrate how GenAI enhances instructional effectiveness and contributes to achieving program goals.

    “Tracking these shifts helps demonstrate how GenAI enhances instructional effectiveness and contributes to achieving program goals.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The MAGE framework was used by educators at Oregon State University and aligned with the six levels of Bloom's Taxonomy to guide reconsideration of course outcomes and student learning in the age of GenAI.

    “This MAGE framework, was utilized by educators at Oregon State University and aligned with the six levels of Bloom's Taxonomy to serve as a guide for reconsidering course outcomes and student learning in the age of GenAI [35].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35] Ecampus OSU. Bloom’s Taxonomy Revisited for the AI Era. Accessed: 2024-10-07 2023. https://ecampus.oregonstate.edu/faculty/artificial-intelligence-tools/blooms-taxonomy-revisited/

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    A recent analysis of seven leading AI content detectors found improvements over earlier versions tested in January 2023, with success rates rising from 66% to near 100% in some cases.

    “A recent analysis of seven leading AI content detectors shows improvements over earlier versions tested in January 2023, with success rates rising from 66% to near 100% in some cases [52].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [52] ZDNet: I Tested 7 AI Content Detectors: They’re Getting Dramatically Better at Identifying Plagiarism. https://www.zdnet.com/article/i-tested-7-ai-content-detectors-theyre-getting-dramatically-better-at-identifying-plagiarism/

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Premisecitation

    The MAGE framework provides a methodical process for evaluating the vulnerability of assessments to AI, specifically focusing on critical thinking skills.

    “This approach provides a methodical process for evaluating the vulnerability of assessments to AI, specifically focusing on critical thinking skills.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Zaphir L, Lodge JM, Lisec J, McGrath D, Khosravi H. How critically can an AI think? A framework for evaluating the quality of thinking of generative artificial intelligence 2024

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Interpretationnone given

    Partnering with education researchers for studies of GenAI impact can help with experimental design, data collection, and analysis, enabling programs to draw meaningful, evidence-based conclusions.

    “Partnering with education researchers for these studies can help in experimental design, data collection, and analysis, enabling programs to draw meaningful, evidence-based conclusions.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Traditional plagiarism detection tools rely on comparisons to previously submitted work, but generative AI submissions often appear novel, making misconduct difficult to detect with certainty.

    “Traditional plagiarism detection tools rely on comparisons to previously submitted work, but with GenAI, student submissions often appear novel, making it difficult to detect misconduct with certainty.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Programs that fail to address academic integrity issues could risk losing credibility with ABET evaluators, who prioritize ethical responsibility as a core student outcome.

    “Programs that fail to address these issues could risk losing credibility with ABET evaluators, who prioritize ethical responsibility as a core student outcome.”
    Quote in the paperSubstantial claim

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

    Interpretationnone given

    Privacy concerns and potential copyright infringement of instructional materials arise particularly when using AI-generated content in teaching and learning.

    “Additionally, challenges such as privacy concerns and potential copyright infringement of instructional materials arise, particularly when using AI-generated content in teaching and learning.”
    Quote in the paperNot a substantial claim

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

    Interpretationnone given

    The rise of GenAI may enable students to use AI-generated content to circumvent the learning process, posing a significant challenge to maintaining coursework integrity.

    “The rise of GenAI presents a significant challenge in education, as it may enable students to use AI-generated content to circumvent the learning process.”
    Quote in the paperSubstantial claim

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

    Interpretationreasoning

    GenAI offers significant potential to enhance learning and assessment practices, which can support programs in demonstrating compliance with ABET's criteria.

    “GenAI offers significant potential to enhance learning and assessment practices, which can support programs in demonstrating compliance with ABET's criteria.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Coordinating contributions from multiple departments and aligning course objectives with diverse disciplinary perspectives required significant effort, and developing shared goals was critical to the course's success.

    “Coordinating contributions from multiple departments and aligning the course objectives with diverse disciplinary perspectives required significant effort.Developing shared goals and ensuring all stakeholders understood the course's aims were critical to its success.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Tasdemir C, Gazo R. Integrating sustainability into higher education curriculum through a transdisciplinary perspective. J Clean Prod. 2020;265: 121759. source

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

    Interpretationreasoning

    GenAI-assisted coding can enable BME students to engage in more hands-on exercises, such as processing and analyzing data, allowing instructors to offer more real-world or research-based problems in class assignments.

    “A prominent example is coding: GenAI-assisted coding can enable BME students to engage in more hands-on exercises, such as processing and analyzing data, allowing instructors to offer more real-world or research-based problems in class assignments.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    Structured assignments that encourage students to engage critically with AI tools may help make courses AI-resistant by shifting focus to understanding, analysis, and creative problem-solving.

    “Furthermore, structured assignments that encourage students to engage critically with AI tools, shown in Case 3.1.2,may help”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The use of real-world projects, such as improving a production line, provided students with hands-on experience and emphasized the practical value of transdisciplinary methods.

    “The use of real-world projects, such as improving a production line, provided students with hands-on experience and emphasized the practical value of transdisciplinary methods.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Tasdemir C, Gazo R. Integrating sustainability into higher education curriculum through a transdisciplinary perspective. J Clean Prod. 2020;265: 121759. source

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

    Premisecitation

    The Purdue course highlighted the importance of horizontal integration of sustainability topics, allowing students to see connections between disciplines and apply their learning to professional decision-making.

    “The course highlighted the importance of horizontal integration of sustainability topics, allowing students to see connections between disciplines and apply their learning to professional decisionmaking.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Tasdemir C, Gazo R. Integrating sustainability into higher education curriculum through a transdisciplinary perspective. J Clean Prod. 2020;265: 121759. source

  • Classified as probably not a substantial claim.

    Interpretationcitation

    By mapping assessments to cognitive tasks, testing AI-generated responses, grading against specific values of inquiry, and evaluating results, instructors can identify risks of AI-produced content circumventing the learning process.

    “By mapping assessments to cognitive tasks, testing AI-generated responses, grading against specific values of inquiry (like accuracy, depth, and relevance), and evaluating the results, instructors can identify the risks of AI-produced content circumventing the learning process.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [34] Zaphir L, Lodge JM, Lisec J, McGrath D, Khosravi H. How critically can an AI think? A framework for evaluating the quality of thinking of generative artificial intelligence 2024

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

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

    Premisenone given

    A/B testing, where one group uses GenAI tools while another relies on conventional methods, offers a controlled approach to identify specific benefits and potential drawbacks.

    “A/B testing, where one group of students uses GenAI tools while another relies on conventional methods, offers a controlled approach to identify specific benefits and potential drawbacks.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    AI bias could lead to certain groups being inadvertently excluded from outreach efforts or receiving content that reinforces stereotypes.

    “This could lead to certain groups being inadvertently excluded from outreach efforts or receiving content that reinforces stereotypes.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI systems are trained on historical data that may contain biases related to gender, race, or socioeconomic status.

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

    Interpretationreasoning

    This highlights the potential for generative AI to help create engaging materials that simplify technical language.

    “This highlights the potential for GenAI to help create engaging materials that simplify technical language.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    This structured approach empowered students to explore creative and practical applications of generative AI while addressing its limitations, fostering deeper understanding of the material.

    “This structured approach empowered students to explore the creative and practical applications of GenAI while addressing its limitations, fostering a deeper understanding of the material.”
    Quote in the paperSubstantial claim
50 of 50

  • The paper relies on this without support.

    An analysis of biomedical engineering job advertisements indicates rising demand for skills in AI-driven data science, real-time data acquisition, and biomedical instrumentation.

    “A recent analysis of job postings in biomedical engineering reveals increasing demand for competencies in AI-driven data science, real-time data acquisition, and biomedical instrumentation.”

  • The paper relies on this without support.

    GenAI is reshaping BME education and will become an integral component of educational practices.

    “The advent of generative artificial intelligence (GenAI) tools is reshaping biomedical engineering (BME) education in profound ways.”
    “GenAI is set to become an integral component of educational practices.”

  • The paper relies on this without support.

    Adding GenAI tools to education creates major worries about academic honesty.

    “Additionally, the integration of GenAI tools raises significant concerns regarding academic integrity.”

  • The paper relies on this without support.

    More students are turning to AI for help with their coursework.

    “As students increasingly rely on AI to assist with assignments, there is a growing need to develop clear guidelines and strategies to ensure that these tools are used ethically and responsibly, without undermining learning outcomes or encouraging academic misconduct.”

  • The paper relies on this without support.

    GenAI can create personalized learning, help students with technical difficulties, and let instructors produce adaptive materials.

    “GenAI offers new opportunities for personalized learning experiences, helping students overcome technical challenges and enabling instructors to generate adaptive materials that cater to individual needs.”

  • The paper relies on this without support.

    GenAI tools bring problems with academic honesty and excessive dependence on AI that may block deeper learning unless managed carefully.

    “However, these tools also introduce challenges related to academic integrity and over-reliance on AI, which can hinder deeper learning if not carefully managed.”

  • The paper relies on this without support.

    Some BME programs cannot add specialized courses because of constraints at local and national levels.

    “While technical depth is essential for developing AI systems, not all BME programs have the flexibility to introduce specialized courses due to both local and national constraints.”

  • The paper relies on this without support.

    Because AI is inherently interdisciplinary, it fits BME's wide scope, which connects engineering, biology, and medicine.

    “However, AI's inherently interdisciplinary nature aligns well with BME's broad scope, bridging engineering, biology, and medicine.”

  • The paper relies on this without support.

    Using health data in AI systems creates major worries about patient privacy and obeying HIPAA.

    “The use of health data in AI systems raises significant concerns, particularly regarding patient privacy and compliance with regulations like the Health Insurance Portability and Accountability Act (HIPAA).”

  • The paper relies on this without support.

    Biomedical devices powered by AI and Internet of Medical Things systems are especially open to cyberattacks.

    “Cybersecurity is another critical aspect, as AI-based biomedical devices and Internet of Medical Things (IoMT) systems are particularly vulnerable to cyberattacks.”

  • The paper relies on this without support.

    Making AI an elective or cocurricular option could leave some students graduating without AI skills that employers now expect.

    “While offering AI as an elective or cocurricular activity is another option, it risks creating disparities among students, where some may graduate without the AI skills now essential in the job market.”

  • The paper relies on this without support.

    Ready-to-use teaching resources can significantly reduce preparation time for faculty without extensive AI backgrounds.

    “Ready-to-use teaching resources, such as pre-built lecture slides, coding exercises, lab modules, pre-configured templates, and datasets specific to BME use cases, can significantly reduce preparation time.”

  • The paper relies on this without support.

    Embedding foundational AI and machine learning concepts into general education or computer science courses provides students with essential knowledge and promotes interdisciplinary learning.

    “Embedding foundational AI and machine learning concepts into general education or computer science courses provides students with essential knowledge and promotes interdisciplinary learning.”

  • The paper relies on this without support.

    Training machine learning models and running simulations demand significant computational resources that may not be available to all institutions.

    “Training machine learning models and running simulations demand significant resources that may not be available to all institutions.”

  • The paper relies on this without support.

    Larger universities often have access to high-performance computing systems and IT support, while smaller institutions face limited infrastructure and expertise.

    “While larger universities often have access to high-performance computing (HPC) systems and IT support, smaller institutions face challenges with limited infrastructure and expertise.”

  • The paper relies on this without support.

    Cloud-based solutions like Google Colab, AWS Educate, and CloudBank offer scalable and cost-effective alternatives that enable broader access to computational tools.

    “Cloud-based solutions like Google Colab, AWS Educate, and CloudBank (https:// www. cloud bank. org/) offer scalable and cost-effective alternatives, enabling broader access to essential computational tools.”

  • The paper relies on this without support.

    Co-teaching models can provide students with a well-rounded understanding of how GenAI tools intersect with various fields, ensuring understanding of technical and social implications.

    “Coteaching models, where faculty from BME, computer science, ethics, business, or law work together to develop and deliver course content, can provide students with a wellrounded understanding of how GenAI tools intersect with various fields, ensuring that students understand both the technical and social implications of AI in medicine.”

  • The paper relies on this without support.

    GenAI can serve as a teaching assistant, assisting with grading and providing feedback on recurring questions and homework assignments, drawing from data trained on previous classes.

    “GenAI can also serve as a teaching assistant (TA), assisting with grading and providing feedback on recurring questions and homework assignments, drawing from data trained on previous classes.”

  • The paper relies on this without support.

    AI systems are trained on historical data that may contain biases related to gender, race, or socioeconomic status.

    “AI systems are trained on historical data, which may contain biases related to gender, race, or socioeconomic status.”

  • The paper relies on this without support.

    AI can enhance outreach by automating content creation, personalizing interactions, and analyzing audience feedback in real time.

    “Leveraging AI in this domain can enhance outreach by automating content creation, personalizing user interactions, and analyzing audience feedback in real-time, enabling more efficient, targeted, and engaging outreach campaigns.”

  • The paper relies on this without support.

    AI can assist engineers in communicating complex BME concepts by analyzing technical papers, simplifying language, generating infographics, and creating video explanations.

    “AI can assist engineers in this process by analyzing technical papers and simplifying language for broader audiences, generating infographics, or even creating video explanations using AI-driven video generators.”

  • The paper relies on this without support.

    AI can make outreach more inclusive by reducing barriers such as language, accessibility, and cultural differences.

    “AI holds the promise of making outreach more inclusive by reducing barriers such as language, accessibility, and cultural differences.”

  • The paper relies on this without support.

    AI tools can translate content into multiple languages, provide real-time closed captioning, and personalize information presentation.

    “AI tools can translate content into multiple languages, provide real-time closed captioning for those with hearing impairments, and even personalize the presentation of information based on user preferences.”

  • The paper relies on this without support.

    AI can analyze large amounts of data to identify underrepresented groups and adjust content strategies to reach diverse communities.

    “Furthermore, AI can analyze large amounts of data to identify underrepresented groups and adjust content strategies to ensure outreach campaigns are reaching diverse communities.”
24 of 24

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.

  • Medical-education case number does not match its heading

    Minor

    The text directs readers to Case 2.2.1.1, while the case itself is labeled 2.2.1.

  • Standards case number does not match its heading

    Minor

    The discussion identifies the example as Case 2.2.4.1, but its heading labels it Case 2.2.4.

  • Seth framework is given two publication years

    Minor

    The discussion dates the cited framework to 2022, but reference 1 lists it as a 2023 publication.

3 of 3

Cites

Flagged first, then by citations

Research works printed but not linked: 6

  1. Hung D. Situated cognition and problem-based learning: Implications for learning and instruction with technology. J Interact Learn Res. 2002;13(4):393–414.
  2. Medicine IE, Society B. Grand challenges at the interface of engineering and medicine. IEEE Open Journal of Engineering in Medicine and Biology (2024). Published by IEEE EMBS with contributions from 50 researchers representing 34 institutions
  3. McGlothlin CW. Assessment of student learning: direct and indirect methods that work. In: ASSE Professional Development Conference and Exposition, 2007:07. ASSE
  4. Williamson B. New digital laboratories of experimental knowledge production: artificial intelligence and education research. London Rev Edu. 2020;18(2):209–20.
  5. Bloom BS. Taxonomy of educational objectives, handbook: the cognitive domain. New York: David McKay; 1956.
  6. The use of chatgpt in teaching and learning: a systematic review. Front Educ. 2023. https://doi.org/10.3389/feduc.2023.1259702. · DOI

Cited by

Sorted by citations

Related

Authors
Khojah, R., Werth, A., Broadhead, K. W., Dobrucki, L. W., Geiger, C., & Rubenstein, D. A.
Affiliations
  • University of California San Diego (US)
  • Cornell University (US)
  • University of Utah (US)
  • University of Illinois Urbana-Champaign (US)
  • University of Florida (US)
  • Stony Brook University (US)
Corresponding author
  • Khojah, Reem
  • Werth, Alexandra
ORCID
Venue
Biomedical Engineering Education
Publisher
Springer Science and Business Media LLC
Published
2025-02-25
Volume
5
Issue
2
Pages
135-151
ISSN
  • 2730-5937 (ISSN-L)
  • 2730-5945
DOI
10.1007/s43683-025-00175-9
OpenAlex
W4407942652
Cited by
9
References
38
Citations by year
2026: 8 · 2025: 1
FWCI
2.19
Type
journal article
Language
en
Update notices
correction (2025-05-05)
Open access
Licence
Locations
Biomedical Engineering Education (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Engineering › Biomedical Engineering › Biomedical and Engineering Education
Keywords
generative AI, AI competencies, curriculum integration, responsible AI use, AI-driven healthcare, personalized learning, academic integrity, overreliance on AI

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 2026-10-05.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-05.

Cited sources are checked by gpt-6-sol.

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.

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