Education
Generative AI rules and work – Nursing education network
In this post, he examines the critical issue of the usage of generative AI (Genai) tools in skilled workplaces. This is especially influential during internships. He emphasizes the potential risk related to “”. Students often unknowingly reveal the corporate’s confidential data to other firms platforms. These platforms lack security at enterprise level. The article emphasizes that while universities are developing AI’s policy for educational use, they didn’t answer properly integrated by work. This significant gap in the rules signifies that each students and employers prone to data violation and reputational damage. Ultimately, the song is in favor of proactive measures. They include the update of existing documents within the workplace and providing training against perception. These steps equip students with ethical and practical knowledge essential for responsible use of artificial intelligence in skilled environments.
Dean, B., Nichola-Richmond, K., Tai, J., I Walton, S. (2025). Do your students know the implications of using artificial intelligence at work? Higher education times.
It will be safely said that many university students use generative artificial intelligence (Genai) of learning tools, university assessments and private use.
- Students widely use Genai for educational, assessment and private use. This comfort of technology naturally extends to their workplaces.
- A major risk results from students using Genai tools and not using a clear permission within the workplace. They may not have knowledge on the workplace, which results in the phenomenon known as “ai shadow”. This behavior, often unintentional, can result in serious data violations.
- Students don’t work maliciously. They often lack the notice of specific ethical boundaries, politicians within the workplace and legal consequences (e.g. privacy laws) to make use of genei with confidential information.
- Most students gain access to Genai tools via personal accounts. In these accounts, they typically do not need solid security at enterprise level. They also don’t offer data management required to service confidential business information.
- Students are guided by the need to performance and impressions on their superiors. Internships often include steep learning curves and unknown systems. There can be time pressure. This makes Genai a tempting “intelligent, efficient movement” to elucidate the tasks, summarize information or design content.
- The university genius policy for educational environments often don’t translate into the study space integrated in work. In addition, receiving organizations may not consider how student interns fit their existing Genai management framework.
- The excellent news is that integrated learning programs at work are well prepared to unravel this problem by developing existing structures.
- Necessary documents, similar to IP contracts, legal contracts, role descriptions and proceedings codes, will be updated to contain clear guidelines for the usage of Genai.
- Online modules, workshops or sessions in school before placing are perfect for discussing ethical principles, risk and duties.
- The first meeting of scholars and superiors is of key importance. It is a possibility to debate the usage of Genai, borders and available support.
How will this affect healthcare students when switching to employment? Perhaps they can be innovators to drive changes and use the potential of artificial intelligence, and is not going to be survived by traditional methods of change. Using AI to the interpretation of data based on proofs of internships of time-frame will be cut off (you could have the option to beat 17 years!).
Genai integration with integrated learning at work is serious challenges. These challenges result primarily from a lack of expertise of information security. There can be a lack of information of the confidentiality and discrepancy of the policy between the tutorial and skilled environment. However, universities and adopting organizations can prepare students to make use of Genai tools “ethically, responsibly and intentionally”. This can achieve this, proactively updating the prevailing program structures. Open communication can be supported to cut back risk and ensure a smooth transition to the long run of labor.
Dean, B., Nichola-Richmond, K., Tai, J., I Walton, S. (2025). Do your students know the implications of using artificial intelligence at work? Higher education times.
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