How to Support Learning and Integrity in Today’s Academic Nursing Programs

In nursing education, the most formidable challenge posed by artificial intelligence is not the technology itself. It is adapting assessment methods, program policies and institutional culture to ensure comprehensive student learning and ethical AI use.

A common fear voiced by educators at large is that AI allows more academic dishonesty. In nursing education, a distinct concern is that AI will dilute learning, resulting in nurses who don’t have the knowledge and critical thinking skills necessary for nursing practice.

These concerns are understandable. But rather than viewing them as a reason to avoid AI, they point to an opportunity for nursing programs to rethink how they define, teach and assess academic integrity today.

“Yes, AI makes it easier to cheat. But AI is not a bad thing,” said Jake Ritz, Director of Exam Security and Incident Investigation for Ascend Learning. ATI is an Ascend Learning brand. “More than anything, AI is a tool that helps us be successful. But we must be knowledgeable about the ways it can be used to gain an advantage, so that we can identify them and take appropriate action.”

How Has AI Changed Academic Integrity in Nursing Education?

Academic dishonesty is not new. Students have always looked for shortcuts, whether it’s writing answers on the bottom of a shoe or sneaking in a cheat sheet. The proliferation of AI has altered the academic dishonesty landscape in two primary ways: It has lowered the obstacles to cheating, and it has increased the sophistication of cheating methods.

Educators must now be vigilant for technology-enabled misconduct. Ritz said strategies and tools are vast and change often. Current examples include:

• plagiarism in the form of AI-written essays and assignments
• web extensions that answer questions during exams or homework
• smart glasses and similar AI service applications
• contract cheating and proxy testers.

To manage these behaviors, some academic programs try AI detection tools. But research suggests they should be used with caution. Studies published in 2025 and 2026 show that AI detectors can produce false positives, perform inconsistently across writing contexts, and disproportionately flag work by nonnative English writers.^1-3

Rather than treating AI detection as proof of misconduct, the authors of these studies suggest that programs view it as one signal to be considered within a broader review process.

The stronger approach, these authors argue, is to combine:

• clear, transparent policies on AI use, including student disclosure
• faculty review of how AI was used
• documentation of students’ writing process
• assessment designs that require application, explanation, reflection, oral defense, simulation, or other forms of demonstrating knowledge.

What Assessment Strategies Reduce AI-Assisted Cheating?

Given that assessments are a pivotal evaluation of student knowledge, their approach and design require new strategies to help prevent academic dishonesty. Ritz said one of the most effective ways faculty can prevent AI-assisted cheating is by adjusting their assessment methods.

Ritz recommends assessing student knowledge in multiple ways. “If you give students an in-class assignment and they do poorly on it, and then they do well on a take-home assignment, you should follow up,” Ritz said. “Ask them, ‘Why were these two assignments so different? What helped you be successful?’ Then you can look for indicators of whether they violated your academic integrity policy.”

This approach does more than reduce opportunities for misconduct. It gives faculty a clearer view of whether students can transfer knowledge into clinical reasoning, communication and decision-making.

In addition to assessing student knowledge in different ways, faculty should place a new emphasis on the experience of learning and how it applies to nursing knowledge and practice.

"With the help of AI, knowledge is now freely available to everyone in every setting," said Sumit Arora, Vice President of Advanced Technology for Ascend Learning. "What we should be looking at now is experiential learning."

Evaluating experiential learning requires assessment designs that ask students to apply or explain what they have learned. Techniques include reflection, oral defense, simulation, and other assessment strategies listed above.

“It’s time to think creatively about assessment methods,” Arora said. “AI can be a helper there. You can create hundreds of case studies and role-play simulations using AI.”

Clear AI Policies Are Fundamental to Academic Integrity

To truly harness the power of AI in any environment, users must go beyond simple adoption. Arora said 3 things are critical to building a strong AI foundation: data, governance, and people.

For academic nursing programs, these pillars can also serve as a practical framework for integrity-focused AI adoption: data practices that protect students, governance that defines acceptable use, and people strategies that prepare faculty and students to make sound decisions.

First, institutions should ensure that student data is accurate, accessible, and ethically managed. Data acts as a foundation for AI and is critical to its successful implementation, Arora said. Integrating data across multiple systems can provide a more complete picture of student performance while maintaining privacy and consent protections.

Second, nursing programs need clear governance frameworks that define how AI can and cannot be used. Policies should address issues such as academic integrity, data privacy, transparency, and fairness. They should also establish guidelines for acceptable AI use by students and faculty.

Third, "The success of AI hinges on the readiness of the people who will interact with it," Arora said. For nursing programs, this means investing in AI literacy for students, faculty and administrators, building consensus around appropriate use, and providing ongoing professional development.

To establish literacy and encourage appropriate use, pay particular attention to:

• definitions of acceptable AI use
• requiring transparency
• communicating with faculty
• consistent enforcement.

“Students are using technology, and they're really wise about it,” Ritz said. “That means educators need to be wise about the technology. They should understand what kind of computers students are using to test on and to study. Make sure your institution is managing those risks well.”

Another important strategy is equipping proctors with a thorough understanding of their role in protecting exam integrity. Technology-related risks are harder to manage when proctors are unsure what to look for, or they are hesitant to interrupt an exam. By setting clear policies and expectations for actions they should take, proctors are more likely to feel they have the authority to act when something appears inconsistent with testing expectations.

“And tell your proctors that if they see something wrong, they should stop the examinee, ask them about it, and ensure that nothing unauthorized is happening,” Ritz said. “This is one of the most important things a proctor can do.”

Why Academic Integrity Requires a Culture Shift

Policies, proctoring and assessment design are essential, but they don’t encompass another key step programs should take. Academic integrity in the age of AI also depends on the culture students experience every day.⁴

How are expectations set and explained? How openly are ethical questions discussed? Do students understand that integrity in the classroom is connected to integrity in clinical practice?

Academic integrity is cultural, not technological. To create a culture of academic integrity, students need to receive clear expectations.

"The thing that institutions can do to help them the most with academic integrity is to focus on the culture at the institution," Ritz said.

AI and Academic Integrity Are Not at Cross Purposes

AI will continue to influence how students learn, how faculty assess knowledge, and how programs define academic integrity. Nursing programs do not need to respond with fear, but they do need a deliberate strategy.

The goal should not be to eliminate AI from learning environments. It is wiser to help students use AI appropriately while ensuring they can demonstrate the knowledge, clinical judgment, and ethical reasoning required for nursing practice.

To be well positioned for future success, programs should combine:

• clear expectations
• assessment variety
• student engagement
• faculty development.

“When you establish a policy on student expectations and you communicate the expectations effectively, you're giving students a stronger chance to be successful,” Ritz said. “And that's really what matters.”  

Normalizing tough topics like cheating, AI use and academic integrity is an effective next step.

“One of the best ways to do this is to actually talk about these issues in class,” Ritz said. “Make the topic of artificial intelligence and the topic of academic integrity safe topics that your students are comfortable speaking about. Once you've communicated expectations, it makes it a lot easier to apply consequences if you need to.”

Read more about integrating academic integrity into classroom teaching in this article: Why You Should Make Academic Integrity Part of Classroom Teaching

References

1. Salamah W. The Role of Artificial Intelligence in Detecting and Preventing Academic Dishonesty in Higher Education: A Systematic Review. Frontiers in Education. 2026. DOI:10.3389/feduc.2026.1880283

2. Deep PD, Edgington WD, Ghosh N, Rahaman MS. Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education. MDMPI. 2025;16(10):905. https://doi.org/10.3390/info16100905

3. Hadra M, Cambridge K, Mesbah M. Evaluating the Accuracy and Reliability of AI Content Detectors in Academic Contexts. International Journal for Educational Integrity. 2026;22(4). https://doi.org/10.1007/s40979-026-00213-1

4. Krueger L, Clemenson S, Johnson E, Schwarz L. ChatGPT in Higher Education; Practical Ideas for Addressing Artificial Intelligence in Nursing Education. Journal of Nursing Education. 2025;64(5):323-326. DOI: 10.3928/01484834-20240424-02