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"5 Tips For Addressing AI in Your Academic Integrity Code" by Carlos Díaz

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"5 Tips For Addressing AI in Your Academic Integrity Code" by Carlos Díaz

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As a librarian and Extended Essay coordinator, the emergence of generative AI in education sent shivers down my spine. Within two days of playing with it, I called a meeting with my Head of School to warn him about the storm to come.

At the time, I feared what others feared: writing was dead, and there would be no point in asking students to write essays anymore. Now I realize that I was overreacting. People have worried about using calculators, the internet, and other technology in schools, and we've adjusted fine.

As AI in education advances, schools need to update their policies to address the challenges and opportunities this new technology brings. Rather than a doomsday device, generative AI-when used ethically -can ease the lives of students and teachers.

With a more realistic view of AI in education, I set about updating our school's academic integrity policy to address the role of generative AI in classrooms.

Here's what I learned and recommend to educators for updating their academic integrity policies in the age of AI.

1. Leverage existing frameworks. Don't reinvent the wheel!

Organizations like International Baccalaureate and Elsevier, universities, and colleges have developed reliable frameworks for using AI in education. Starting from scratch doesn't make sense, so consider their guidance when writing your school's AI policy.

If your school follows the International Baccalaureate or College Board-sanctioned curricula, you must follow their recently updated guidelines. If your school doesn't use either, you can get directions from the MLA and APA citation guidelines. Quote them in your policy, and rely on the work of reputed organizations.

2. Tailor AI policies for your school

When updating your school's or district's academic integrity policy to address AI, you'll want to consider what makes the most sense for your students and teachers.

There is no universally agreed-upon set of hard-and-fast rules for exactly what is and isn't ethical when using AI in education. One teacher might think using AI to proofread an essay is cheating, while another teacher might fully support it. So, choose what works for your school or district's needs, values, and goals.

Here are specific areas of ethical ambiguity to consider when crafting your school's policy:

Transparency and education. Ensure students understand acceptable and forbidden uses of AI within your school environment. Our policy explicitly states that generative AI can be used for brainstorming, but students must ultimately own their writing and critical thinking.

Clear citation expectations. Clarify how and when students must cite generative AI sources. In the Student Responsibilities section of our policy, we added a specific clause: "Students must use assistive technologies like generative AI responsibly, acknowledging their contributions and never claiming AI-generated work as their own."

By implementing and adapting these recommendations to your specific context, you can ensure a smooth integration of AI tools that foster ethical learning and empower students to become responsible digital citizens.
3. Provide training and support on AI
As a school community, your administration should provide workshops and professional learning opportunities about artificial intelligence for staff and students so everybody knows what to expect.

In our school guidelines, we added that students and teachers can anticipate the following: "The school will guide teachers and students in the ethical use of generative AI and assistive technology."

This reflects our school's commitment to setting up our teachers and students for success with navigating new AI tools and technology. Training and support on ethical AI usage can delve into these specific areas:

If you provide training and support on using AI, you can foster a culture of responsible use and continuous learning that will empower teachers and students alike.
4. Embrace the ever-evolving nature of AI in education
Make sure your policy is reviewed regularly, at least once per year. If the sudden boom of AI in education has taught us anything, technology is only accelerating. More and more powerful AI tools will emerge in the coming years. Your policy should be ready to adapt to these massive shifts in how we do things.

Here are some specific tips for making your policy more flexible:

These tips will help you develop a flexible policy to adapt to AI's ever-changing landscape in education.
5. Shift your focus from AI policing to growth-oriented assessments
While relying on AI detectors to monitor student behavior might seem tempting, they often prove to be more trouble than they're worth. Inaccurate and intrusive, these technologies are best avoided as a policing tool.
Instead, let's focus on designing assessments that foster learning and measure progress, not just outcomes.

When we prioritize growth instead of just focusing on results, we develop assessments that are naturally more resistant to academic dishonesty. This approach promotes a more positive learning environment and yields more meaningful insights into student development.
Adapting academic integrity policies in the AI era
Updating educational integrity policies to address the challenges and opportunities presented by AI is crucial in making sure students use technology ethically in education.

Schools can create robust policies that promote academic integrity while leveraging the benefits of AI in education if they follow the tips outlined in this article, such as leveraging existing frameworks, emphasizing ethics, providing training, acknowledging the variability of the situation, and avoiding detectors.

As we foster a learning environment that upholds integrity and intellectual honesty, we will prepare students for future success, no matter how AI shapes the world.

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5 Tips For Addressing AI in Your Academic Integrity Code

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As a librarian and Extended Essay coordinator, the emergence of generative AI in education sent shivers down my spine. Within two days of playing with it, I called a meeting with my Head of School...

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"How Do We Maintain Academic Integrity in the ChatGPT Era?" by Tricia Bertram Gallant 

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"How Do We Maintain Academic Integrity in the ChatGPT Era?" by Tricia Bertram Gallant 

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How Do We Maintain Academic Integrity in the ChatGPT Era? | AAC&U

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(Hint: It’s really no different from what we should have been doing all along)

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Conference on College Composition and Communication

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This joint task force has been convened by the leadership of the Conference on College Composition and Communication (CCCC) and the Modern Language Association (MLA) to develop resources, guidelines, and professional standards around the use of AI and writing.

The MLA-CCCC Joint Task Force on Writing and AI Working Paper: Overview of the Issues, Statement of Principles, and Recommendations is the first working paper developed by the task force. It discusses the benefits and risks of generative AI as it relates to writing, literature, and language programs.
Read the task force's first working paper (PDF).
Recently, the task force created Exploring AI Pedagogy: A Community Collection of Teaching Reflections, offering a space for ELA teachers to hold informal discussions and share practical, frank reflections to help each other better reach students and continuously improve pedagogy. This project is open to all for contributions and feedback.

Task Force Members
Antonio Byrd
Leonardo Flores
Angela Gibson (MLA staff liaison)
David Green
Holly Hassel (co-chair)
Sarah Z. Johnson
Matthew Kirschenbaum
A. Lockett
Elizabeth Mathews Losh (co-chair)
Anna Mills

Learn more about the task force.

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MLA-CCCC Joint Task Force on Writing and AI - Conference on College Composition and Communication

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This joint task force has been convened by the leadership of the Conference on College Composition and Communication (CCCC) and the Modern Language Association (MLA) to develop resources, guidelines, and professional standards around the use of AI and writing. The MLA-CCCC Joint Task Force on Writing and AI Working Paper: Overview of the Issues, Statement [...]

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The MLA's method for citing sources uses a template of core elements-standardized criteria that writers can use to evaluate sources and create works-cited-list entries based on that evaluation. That new technologies like ChatGPT emerge is a key reason why the MLA has adopted this approach to citation-to give writers flexibility to apply the style when they encounter new types of sources. In what follows, we offer recommendations for citing generative AI, defined as a tool that "can analyze or summarize content from a huge set of information, including web pages, books and other writing available on the internet, and use that data to create original new content" (Weed).

You should

cite a generative AI tool whenever you paraphrase, quote, or incorporate into your own work any content (whether text, image, data, or other) that was created by it

acknowledge all functional uses of the tool (like editing your prose or translating words) in a note, your text, or another suitable location

take care to vet the secondary sources it cites (see example 5 below for more details)

See below for specific examples. And keep in mind: the MLA template of core elements is meant to provide flexibility in citation. So if you find a rationale to modify these recommendations in your own citations, we encourage you to do so. We've opened this post up for commenting, so let us know what you think and how you're using and citing generative AI tools!

Using the MLA Template

Author

We do not recommend treating the AI tool as an author. This recommendation follows the policies developed by various publishers, including the MLA's journal PMLA.

Title of Source

Describe what was generated by the AI tool. This may involve including information about the prompt in the Title of Source element if you have not done so in the text.

Title of Container

Use the Title of Container element to name the AI tool (e.g., ChatGPT).

Version

Name the version of the AI tool as specifically as possible. For example, the examples in this post were developed using ChatGPT 3.5, which assigns a specific date to the version, so the Version element shows this version date.

Publisher

Name the company that made the tool.

Date

Give the date the content was generated.

Location

Give the general URL for the tool.

Example 1: Paraphrasing Text

Passage in Source

Paraphrased in Your Prose

While the green light in The Great Gatsby might be said to chiefly symbolize four main things: optimism, the unattainability of the American dream, greed, and covetousness ("Describe the symbolism"), arguably the most important-the one that ties all four themes together-is greed.

Works-Cited-List Entry

"Describe the symbolism of the green light in the book by F. Scott Fitzgerald" prompt. The Great Gatsby ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

Example 2: Quoting Text

Passage in Source

Quoted in Your Prose

When asked to describe the symbolism of the green light in The Great Gatsby, ChatGPT provided a summary about optimism, the unattainability of the American dream, greed, and covetousness. However, when further prompted to cite the source on which that summary was based, it noted that it lacked "the ability to conduct research or cite sources independently" but that it could "provide a list of scholarly sources related to the symbolism of the green light in The Great Gatsby " ("In 200 words").

Works-Cited-List Entry

"In 200 words, describe the symbolism of the green light in The Great Gatsby" follow-up prompt to list sources. ChatGPT, 13 Feb. version, OpenAI, 9 Mar. 2023, chat.openai.com/chat.

While we've provided fairly detailed descriptions of the prompts above, a more general one (e.g., Symbolism of the green light in The Great Gatsby prompt) could be used, since you are describing something that mimics a conversation, which could have various prompts along the way.

Example 3: Citing Creative Visual Works

If you are incorporating an AI-generated image in your work, you will likely need to create a caption for it following the guidelines in of the MLA Handbook. Use a description of the prompt, followed by the AI tool, version, and date created:

Fig. 1. "Pointillist painting of a sheep in a sunny field of blue flowers" prompt, DALL-E, version 2, OpenAI, 8 Mar. 2023, labs.openai.com/.

You can use this same information if you choose to create a works-cited-list entry instead of including the full citation in the caption (see MLA Handbook, sec. 1.7).

Example 4: Quoting Creative Textual Works

If you ask a generative AI tool to create a work, like a poem, how you cite it will depend on whether you assign a title to it. Let's say, for example, you ask ChatGPT to write a villanelle titled "The Sunflower" that-you guessed it!-describes a sunflower and then quote it in your text. Your works-cited-list entry might look like this:

"The Sunflower" villanelle about a sunflower. ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

If you did not title the work, incorporate part of or all of the first line into the description of the work in the Title of Source element:

"Upon the shore . . ." Shakespearean sonnet about seeing the ocean. ChatGPT, 13 Feb. version, OpenAI, 8 Mar. 2023, chat.openai.com/chat.

For guidance on using descriptions and text from the work itself in the Title of Source element, see the MLA Handbook, 5.28 and 5.29.

Example 5: Citing Secondary Sources Used by an AI Tool

You should also take care to vet the secondary sources cited by a generative AI tool-with the caveat that AI tools do not always cite sources or, when they do, do not always indicate precisely what a given source has contributed. If you cite an AI summary that includes sources and do not go on to consult those sources yourself, we recommend that you acknowledge secondary sources in your work.

For example, let's say that you ask Bing AI to explain the concept of the political unconscious, citing sources, and it provides the following answer:

Let's say that you then decide to quote from the final sentence. You need to click through to the source listed in the note in order to get more information than just a URL for the source. There, you will read the following:

Now, you can treat Oxford Reference as your source since Bing AI was merely a research conduit to the source (see MLA Handbook 5.34 for more information). If for some reason you want to treat a source cited in a generative AI tool as an indirect source-and you know it is, in fact, the source for the information provided by the AI, follow the guidance in section 6.77 of the MLA Handbook.

Note

1. At the time of writing this post, ChatGPT doesn't have a built-in feature to create a unique URL to the conversation. However, an outside tool like the Chrome extension ShareGPT can generate such a link. If you use that type of outside tool, include the unique URL that the tool generates instead of the general URL.

DALL-E allows users to download the AI-generated images they create or generate a publicly-available URL that leads to an image. If you choose to create a shareable link for an image you generate with DALL-E (or other similar AI image generators), include that unique URL that leads to the image instead of the general URL.

Works Cited

MLA Handbook. 9th ed., Modern Language Association of America, 2021. MLA Handbook Plus, 2021, mlahandbookplus.org/.

Weed, Julie. "Can ChatGPT Plan Your Vacation?" The New York Times, 16 Mar. 2023, www.nytimes.com/2023/03/16/travel/chatgpt-artificial-intelligence-travel-vacation.html.

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How do I cite generative AI in MLA style? | MLA Style Center

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The MLA's method for citing sources uses a template of core elements-standardized criteria that writers can use to evaluate sources and create works-cited-list entries based on that evaluation. That new technologies like ChatGPT emerge is a key reason why the MLA has adopted this approach to citation-to give writers flexibility to apply the style when ...

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Cite This Webpage

McAdoo, T. (2024, February 23). How to cite ChatGPT. APA Style. https://apastyle.apa.org/blog/how-to-cite-chatgpt

We, the APA Style team, are not robots. We can all pass a CAPTCHA test, and we know our roles in a Turing test. And, like so many nonrobot human beings this year, we've spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT. We've also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we'll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you've used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT "chat" are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications, with ChatGPT-generated text there is no person communicating. Quoting ChatGPT's text from a chat session is therefore more like sharing an algorithm's output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with "Is the left brain right brain divide real or a metaphor?" the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, "the notation that people can be characterized as 'left-brained' or 'right-brained' is considered to be an oversimplification and a popular myth" (OpenAI, 2023).
Reference
OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of "What is a more accurate representation?" the ChatGPT-generated text indicated that "different brain regions work together to support various cognitive processes" and "the functional specialization of different regions can change in response to experience and environmental factors" (OpenAI, 2023; see Appendix A for the full transcript).
Reference
OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

Parenthetical citation: (OpenAI, 2023)

Narrative citation: OpenAI (2023)

Let's break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is "ChatGPT," so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using "ChatGPT" as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what's being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor "Large language model" in square brackets. OpenAI describes ChatGPT-4 as a "large multimodal model," so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat. For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher's homepage).

Other questions about citing ChatGPT

You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references-four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model's interpretation of them.

We've also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications?

On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.

For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.

We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.

Reference

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

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This post outlines how to create references for large language model AI tools like ChatGPT and how to present AI-generated text in a paper.

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Chicago Manual of Style: Citing Generative AI

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Chicago Manual of Style: Citing Generative AI

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Q. How do you recommend citing content developed or generated by artificial intelligence, such as ChatGPT? Many scholarly publishers are requiring its identification though also requiring human authors to take responsibility for it and will not permit the AI to have "authorship."

A. You do need to credit ChatGPT and similar tools whenever you use the text that they generate in your own work. But for most types of writing, you can simply acknowledge the AI tool in your text (e.g., "The following recipe for pizza dough was generated by ChatGPT").

If you need a more formal citation-for example, for a student paper or for a research article- a numbered footnote or endnote might look like this:

1. Text generated by ChatGPT, OpenAI, March 7, 2023, https://chat.openai.com/chat.

ChatGPT stands in as "author" of the content, and OpenAI (the company that developed ChatGPT) is the publisher or sponsor, followed by the date the text was generated. After that, the URL tells us where the ChatGPT tool may be found, but because readers can't necessarily get to the cited content (see below), that URL isn't an essential element of the citation.

If the prompt hasn't been included in the text, it can be included in the note:

1. ChatGPT, response to "Explain how to make pizza dough from common household ingredients," OpenAI, March 7, 2023.

If you've edited the AI-generated text, you should say so in the text or at the end of the note (e.g., "edited for style and content"). But you don't need to say, for example, that you've applied smart quotes or adjusted the font; changes like those can be imposed silently (see and 13.8).

If you're using author-date instead of notes, any information not in the text would be placed in a parenthetical text reference. For example, "(ChatGPT, March 7, 2023)."

But don't cite ChatGPT in a bibliography or reference list unless you provide a publicly available link (e.g., via a browser extension like ShareGPT or A.I. Archives). Though OpenAI assigns unique URLs to conversations generated from your prompts, those can't be used by others to access the same content (they require your login credentials), making a ChatGPT conversation like an email, phone, or text conversation-or any other type of personal communication (see and 15.53).

To sum things up, you must credit ChatGPT when you reproduce its words within your own work, but unless you include a publicly available URL, that information should be put in the text or in a note-not in a bibliography or reference list. Other AI-generated text can be cited similarly. Check back with us for updates on this evolving topic.

For some considerations on the use of AI in scholarly publishing and the responsibilities of authors, start with this position statement on authorship and AI tools from COPE (Committee on Publication Ethics).

[ Editor's update: Original answer updated to put the publisher before the date and to acknowledge tools like ShareGPT and A.I. Archives.]

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The Chicago Manual of Style, 17th Edition

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Find it. Write it. Cite it. The Chicago Manual of Style Online is the venerable, time-tested guide to style, usage, and grammar in an accessible online format. ¶ It is the indispensable reference for writers, editors, proofreaders, indexers, copywriters, designers, and publishers, informing the editorial canon with sound, definitive advice.

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Author Guidelines for Artificial Intelligence (AI)-Generated Text

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"My students are using AI to cheat. Here’s why it’s a teachable moment" by Siva Vaidhyanathan

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"My students are using AI to cheat. Here’s why it’s a teachable moment" by Siva Vaidhyanathan

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My students are using AI to cheat. Here’s why it’s a teachable moment

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Ignoring ChatGPT and its cousins won’t get us anywhere. In fact, these systems reveal issues we too often miss

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I am reposting my comment from the chat: One issue with ethical use is that Generative AI is using an enormous (mind-blowing) amount of natural resources, and was trained unethically on people’s writing without their consent and often in violation of copyright. We may have ethical issues with using it at all, and with training it further by using it and asking students to use it.

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Ethical Use of Automated, Data Driven Systems is perhaps too narrow, we perhaps need a framework that actually names "epistemic rights"

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Ethical Use of Automated, Data Driven Systems is perhaps too narrow, we perhaps need a framework that actually names "epistemic rights"

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We have to actively create healthy, public, safe spaces for everyone in these spaces. Situating ethical concerns in this space feels more natural than trying to have a conversation about how to ethically use AI in the discourse which is currently driven by corporations.

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Abstract

In contrast to China's enormous efforts to upgrade its system of governance to a new technological level built around a stupefying amount of data collection and electronic scoring, countries committed to democracy and human rights did not upgrade their systems. Instead of adjusting democracy and human rights to the new technological possibilities, those countries ended up with surveillance capitalism. It is vital for the sheer survival of those ideas about governance to perform such an upgrade. The present project aims to contribute to that. I propose a framework of epistemic actorhood in terms of four roles, and characterize digital lifeworlds and what matters about them, in terms of both how they fit in with Max Tegmark's distinction among various stages of human life and how they give rise to their own episteme and the data episteme, with its immense possibilities of infopower (vocabulary inspired by Foucault). A set of epistemic rights that strengthen existing human rights-as part of a fourth generation of rights-is needed to protect epistemic actorhood in those roles, which would be a long way towards performing this kind of upgrade. In the long run, as we progress into Life 3.0, we need a new kind of human right, the right to the exercise of genuinely human intelligence. The good news is that, to the extent that we can substantiate the meaning of human life in the uncaring world that natural science describes, we can substantiate such a right vis-à-vis nonhuman intelligent life. We must hope that arguments of this sort can persuade a superior intelligence-which is by definition, massively beyond ours, and hard to anticipate.

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The Fourth Generation of Human Rights: Epistemic Rights in Digital Lifeworlds

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In contrast to China's enormous efforts to upgrade its system of governance to a new technological level built around a stupefying amount of data collection and electronic scoring, countries committed to democracy and human rights did not upgrade their systems. Instead of adjusting democracy and human rights to the new technological possibilities, those countries ended up with surveillance capitalism.

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Learning activity for AI ethics for industry contexts

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Learning activity for AI ethics for industry contexts

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IBM SkillsBuild has a free course, AI Ethics, which covers what they call the 5 Pillars of AI Ethics. It does not cover all of the bigger "human" and environmental issues but provides an understanding for students who are going into industries--marketing, healthcare management, technology, law enforcement, retail--where they will be using AI tools and be part of decision-making or product teams. I would recommend for students in business, CIS fields, data science, even psychology since many grads will be employed in areas like HR or healthcare mgmt. (The link can only be accessed, unfortunately, after you log in.)

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There are some interesting activities here--I've tried the Critical Assessment and Analysis one.

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Elisa Beshero-Bondar Penn State University

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This study reflects my biggest concerns. I'm on the fence in many ways--I think these tools are here and we need to find ways to teach students about what they are, how to use them and learn about issues like algorithmic bias. Technology is not neutral and Gen AI in education will lead to an even bigger digital divide. I teach an intro to STS and my students and I approach the tools critically, even when we use them as part of assignments. While I have cautiously incorporated some AI use in my teaching, I have a fundamental ambivalence about whether "ethical" AI is even possible and I share that ambivalence with my students.

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Introduction

Artificial intelligence (AI) is a vast technology used in the education sector. Several types of AI technology are used in education (Nemorin et al., 2022). Majorly includes Plagiarism Detection, Exam Integrity (Ade-Ibijola et al., 2022), Chatbots for Enrollment and Retention (Nakitare and Otike, 2022), Learning Management Systems, Transcription of Faculty Lectures, Enhanced Online Discussion Boards, Analyzing Student Success Metrics, and Academic Research (Nakitare and Otike, 2022). Nowadays, Education Technology (EdTech) companies are deploying emotional AI to quantify social and emotional learning (McStay, 2020). Artificial intelligence, affective computing methods, and machine learning are collectively called "emotional AI" (AI). Artificial intelligence (AI) shapes our future more powerfully than any other century's invention. Anyone who does not understand it will soon feel left behind, waking up in a world full of technology that feels more and more like magic (Maini and Sabri, 2017). Undoubtedly, AI technology has significant importance, and its role has been witnessed in the recent pandemic. Many researchers agree it can be essential in education (Sayed et al., 2021). but this does not mean it will always be beneficial and free from ethical concerns (Dastin, 2018). Due to this, many researchers focus on its development and use but keep their ethical considerations in mind (Justin and Mizuko, 2017). Some believe that although the intentions behind AI in education may be positive, this may not be sufficient to prove it ethical (Whittaker and Crawford, 2018).

There is a severe need to understand the meaning of being "ethical" in the context of AI and education. It is also essential to find out the possible unintended consequences of the use of AI in education and the main concerns of AI in education, and other considerations. Generally, AI's ethical issues and concerns are innovation cost, consent issues, personal data misuse, criminal and malicious use, freedom and autonomy loss, and the decision-making loss of humans, etc. (Stahl B. C., 2021a, 2021b). Although, technology also enhances organizational information security (Ahmad et al., 2021) and competitive advantage (Sayed and Muhammad, 2015) and enhances customer relationships (Rasheed et al., 2015). Researchers are afraid that by 2030 the AI revolution will focus on enhancing benefits and social control but will also raise ethical concerns, and there is no consensus among them. A clear division regarding AI's positive impact on life and moral standing (Rainie et al., 2021).

It is evident from the literature on the ethics of AI that besides its enormous advantages, many challenges also emerge with the development of AI in the context of moral values, behavior, trust, and privacy, to name a few. The education sector faces many ethical challenges while implementing or using AI. Many researchers are exploring the area further. We divide AI in education into three levels. First, the technology itself, its manufacturer, developer, etc. The second is its impact on the teacher, and the third is on the learner or student.

Foremost, there is a need to develop AI technology for education, which cannot be the basis of ethical issues or concerns (Ayling and Chapman, 2022). The high expectations of AI have triggered worldwide interest and concern, generating 400+ policy documents on responsible AI. Intense discussions over ethical issues lay a helpful foundation, preparing researchers, managers, policymakers, and educators for constructive discussions that will lead to clear recommendations for building reliable, safe, and trustworthy systems that will be a commercial success (Landwehr, 2015). But the question is, is it possible to develop an AI technology for education that will never cause an ethical concern? Maybe the developer or the manufacturer has dishonest gain from the AI technology in education. Maybe their intentions are not towards the betterment and assistance of education. Such questions come to mind when someone talks about the impact of AI in Education. Even if the development of AI technology is clear from any ethical concerns from the developer or manufacturer, there is no guarantee for the opposite view. The risk of ethical considerations will also rely upon the technical quality. Higher quality will minimize the risk but is it possible for all educational institutions to implement expensive technology of higher quality? (Shneiderman, 2021). Secondly, many issues may arise when teachers use AI technology (Topcu and Zuck, 2020). It may be security, usage, implementation, etc. Questions about security, bias, affordability, trust, etc., come to mind (IEEE, 2019). Thirdly, privacy, trust, safety, and health issues exist at the user level. To address such questions, a robust regulatory framework and policies are required. Still, unfortunately, no framework has been devised, no guidelines have been agreed upon, no policies have been developed, and no regulations have been enacted to address the ethical issues raised by AI in education (Rosé et al., 2018).

It is evident that AI technology has many concerns (Stahl B. C., 2021a, 2021b), and like other sectors, the education sector is also facing challenges (Hax, 2018). If not all the issues/problems directly affect education and learning, most directly or indirectly impact the education process. So, it is difficult to decide whether AI has a positive ethical impact on education or negative or somewhat positive or negative. The debate on ethical concerns about AI technology will continue from case to case and context to context (Petousi and Sifaki, 2020). This research is focused on the following three moral fears of AI in education:

1. 1.
Security and privacy

2. 2.
Loss of human decision-making

3. 3.
Making humans lazy

Although many other concerns about AI exist in education, these three are the most common and challenging in the current era. Additionally, no researcher can broaden the study beyond the scope.

Theoretical discussion

AI in education

AI is based on computer programming or computational approaches; questions can be raised on the process of data analysis, interpretation, sharing, and processing (Holmes et al., 2019) and how the biases should be prevented, which may impact the rights of students as it is believed that design biases may increase with time and how it will address concerns associated with gender, race, age, income inequality, social status, etc. (Tarran, 2018). Like any other technology, there are also some challenges related to AI and its application in education and learning. This paper focuses on the ethical concerns of AI in education. Some problems are related to privacy, data access, right and wrong responsibility, and student records, to name a few (Petousi and Sifaki, 2020). In addition, data hacking and manipulation can challenge personal privacy and control; a need exists to understand the ethical guidelines clearly (Fjelland, 2020).

Perhaps the most important ethical guidelines for developing educational AI systems are well-being, ensuring workplace safety, trustworthiness, fairness, honoring intellectual property rights, privacy, and confidentiality. In addition, the following ten principles were also framed (Aiken and Epstein, 2000).

1. 1.
Ensure encouragement of the user.

2. 2.
Ensure safe human-machine interaction and collaborative learning

3. 3.
Positive character traits are to be ensured.

4. 4.
Overloading of information to be avoided

5. 5.
Build an encouraging and curious learning environment

6. 6.
Ergonomics features to be considered

7. 7.
Ensure the system promotes the roles and skills of a teacher and never replaces him

8. 8.
Having respect for cultural values

9. 9.
Ensure diversity accommodation of students

10. 10.
Avoid glorifying the system and weakening the human role and potential for growth and learning.

If the above principles are discussed individually, many questions arise while using AI technology in education. From its design and planning to use and impact, at every stage, ethical concerns arise and are there. It's not the purpose for which AI technology is developed and designed. Technology is advantageous for one thing but dangerous for another, and the problem is how to disintegrate the two (Vincent and van, 2022).

In addition to the proper framework and principles not being followed during the planning and development of AI for Education, bias, overconfidence, wrong estimates, etc., are additional sources of ethical concerns.

Security and privacy issues

Stephen Hawking once said that success in creating AI would be the most significant event in human history. Unfortunately, it might also be the last unless we learn to avoid the risks. Security is one of the major concerns associated with AI and learning (Köbis and Mehner, 2021). Trust-worthy artificial intelligence (AI) in education: Promises and challenges (Petousi and Sifaki, 2020; Owoc et al., 2021). Most educational institutions nowadays use AI technology in the learning process, and the area attracted researchers and interests. Many researchers agree that AI significantly contributes to e-learning and education (Nawaz et al. 2020; Ahmed and Nashat, 2020). Their claim is practically proved by the recent COVID-19 pandemic (Torda, 2020; Cavus et al., 2021). But AI or machine learning also brought many concerns and challenges to the education sector, and security and privacy are the biggest.

No one can deny that AI systems and applications are becoming a part of classrooms and education in one form or another (Sayantani, 2021). Each tool works according to its way, and the student and teacher use it accordingly. It creates an immersive learning experience using voices to access information and invites potential privacy and security risks (Gocen and Aydemir, 2020). While answering a question related to privacy concerns focuses on student safety as the number one concern of AI devices and usage. The same may go for the teacher's case as well.

Additionally, teachers know less about the rights, acts, and laws of privacy and security, their impact and consequences, and any violations cost to the students, teachers, and country (Vadapalli, 2021). Machine learning or AI systems are purely based on data availability. Without data, it is nothing, and the risk is unavoidable of its misuse and leaks for a lousy purpose (Hübner, 2021).

AI systems collect and use enormous data for making predictions and patterns; there is a chance of biases and discrimination (Weyerer and Langer, 2019). Many people are now concerned with the ethical attributes of AI systems and believe that the security issue must be considered in AI system development and deployment (Samtani et al., 2021). The Facebook-Cambridge Analytica scandal is one of the significant examples of how data collected through technology is vulnerable to privacy concerns. Although much work has been done, as the National Science Foundation recognizes, much more is still necessary (Calif, 2021). According to Kurt Markley, schools, colleges, and universities have big banks of student records comprising data related to their health, social security numbers, payment information, etc., and are at risk. It is necessary that learning institutions continuously re-evaluate and re-design the security practices to make the data secure and prevent any data breaches. The trouble is even more in remote learning environments or when information technology is effective (Chan and Morgan, 2019).

Human interactions with AI are increasing day by day. For example, various AI applications, like robots, chatbots, etc., are used in e-learning and education. Many will learn human-like habits one day, but some human attributes, like self-awareness, consciousness, etc., will remain a dream. AI still needs data and uses it for learning patterns and making decisions; privacy will always remain an issue (Mhlanga, 2021). On the one hand, it is a fact that AI systems are associated with various human rights issues, which can be evaluated from case to case. AI has many complex pre-existing impacts regarding human rights because it is not installed or implemented against a blank slate but as a backdrop of societal conditions. Among many human rights that international law assures, privacy is impacted by it (Levin, 2018). From the discussed review, we draw the following hypothesis.

H ₁: There is a significant impact of artificial intelligence on the security and privacy issues

Making humans lazy

AI is a technology that significantly impacts Industry 4.0, transforming almost every aspect of human life and society (Jones, 2014). The rising role of AI in organizations and individuals feared the persons like Elon Musk and Stephen Hawking. Who thinks it is possible when AI reaches its advanced level, there is a risk it might be out of control for humans (Clark et al., 2018). It is alarming that research increased eight times compared to the other sectors. Most firms and countries invest in capturing and growing AI technologies, skills, and education (Oh et al., 2017). Yet the primary concern of AI adoption is that it complicates the role of AI in sustainable value creation and minimizes human control (Noema, 2021).

AI is minimizing our autonomous role, replacing our choices with its choices, and making us lazy in various walks of life (Danaher, 2018). It is argued that AI undermines human autonomy and responsibilities, leading to a knock-out effect on happiness and fulfilment (C. Eric, 2019). The impact will not remain on a specific group of people or area but will also encompass the education sector. Teachers and students will use AI applications while doing a task/assignment, or their work might be performed automatically. Progressively, getting an addiction to AI use will lead to laziness and a problematic situation in the future. To summarize the review, the following hypothesis is made:

H ₂: There is a significant impact of artificial intelligence on human laziness

Loss of human decision-making

Technology plays an essential role in decision-making. It helps humans use information and knowledge properly to make suitable decisions for their organization and innovations (Ahmad, 2019). Humans are producing large volumes of data, and to make it efficient, firms are adopting and using AI and kicking humans out of using the data. Humans think they are getting benefits and saving time by using AI in their decisions. But it is overcoming the human biological processors through lowing cognition capabilities (Jarrahi, 2018).

It is a fact that AI technologies and applications have many benefits. Still, AI technologies have severe negative consequences, and the limitation of their role in human decision-making is one of them. Slowly and gradually, AI limits and replaces the human role in decision-making. Human mental capabilities like intuitive analysis, critical thinking, and creative problem-solving are getting out of decision-making (Ghosh et al., 2019). Consequently, this will lead to their loss as there is a saying, use it or lose it. The speed of adaptation of AI technology is evident from the usage of AI in the strategic decision-making processes, which has increased from 10 to 80% in five years (Sebastian and Sebastian, 2021).

The role of AI in decision-making in educational institutions is spreading daily. Universities are using AI in both academic and administrative activities. From students searching for program admission requirements to the issuance of degrees, they are now assisted by AI personalization, tutoring, quick responses, 24/7 access to learning, answering questions, and task automation are the leading roles AI plays in the education sector (Karandish, 2021).

In all the above roles, AI collects data, analyzes it, and then responds, i.e., makes decisions. It is necessary to ask some simple but essential questions: Does AI make ethical choices? The answer is AI was found to be racist, and its choice might not be ethical (Tran, 2021). The second question is, does AI impact human decision-making capabilities? While using an intelligent system, applicants may submit their records directly to the designer and get approval for admission tests without human scrutiny. One reason is that the authorities will trust the system; the second may be the laziness created by task automation among the leaders.

Similarly, in keeping the records of students and analyzing their data, again, the choice will be dependent on the decision made by the system, either due to trust or due to the laziness created by task automation among the authorities. Almost in every task, the teachers and other workers lose the power of cognition while making academic or administrative decisions. And their dependency increases daily on the AI systems installed in the institution. To summarize the review, in any educational organization, AI makes operations automatic and minimizes staff participation in performing various tasks and making decisions. The teachers and other administrative staff are helpless in front of AI as the machines perform many of their functions. They are losing the skills of traditional tasks to be completed in an educational setting and consequently losing the reasoning capabilities of decision-making.

H ₃: There is a significant impact of artificial intelligence with the loss of human decision making

Conceptual framework

Fig. 1

Methodology

Research design

The research philosophy focuses on the mechanism of beliefs and assumptions regarding knowledge development. It is precisely what the researcher works on while conducting research and mounting expertise in a particular area. In this research, the positivist philosophy of analysis is used. Positivism focuses on an observable social reality that produces the laws, just like generalizations. This philosophy uses the existing theory for hypotheses development in this study.

Furthermore, this philosophy is used because this study is about measurable and quantifiable data. The quantitative method is followed for data collection and analysis in this research. The quantitative practice focuses on quantifiable numbers and provides a systematic approach to assessing incidences and their associations. Moreover, while carrying out this study, the author evaluated the validity and reliability tools to ensure rigor in data. The primary approach is used because the data collected in this research is first-hand, which means it is collected directly from the respondents.

Sample and sampling techniques

The purposive sampling technique was used in this study for the primary data collection. This technique is used because it targets a small number of participants to participate in the survey, and their feedback shows the entire population (Davies and Hughes, 2014). Purposive sampling is a recognized non-probabilistic sampling technique because the author chose the participants based on the study's purpose. The respondents of this study were students at different universities in Pakistan and China. Following the ethical guidelines, consent was taken from the participants. After that, they were asked to give their responses through a questionnaire. The number of participants who took part in the study was 285. This data collection was around two months, from 4 July 2022 to 31 August 2022.

Measures

Common method bias

Reliability and validity of the data

Reliability and validity confirm the health of the instrument and survey data for further analysis. Two tools are used in structural equation modeling for reliability: item reliability and construct reliability. The outer loading of each item gauges the item's reliability. Its threshold value is 0.706, but in some cases, even 0.5 is also acceptable if the basic assumption of the convergent validity is not violated (Hair and Alamer, 2022). Cronbach's Alpha and composite reliability are the most used tools to measure construct reliability. The threshold value is 0.7 (Hair Jr et al., 2021). Table 3 shows that all the items of each construct have outer loading values greater than 0.7. Only one item of artificial intelligence and one item of decision making is below 0.7 but within the minimum limit of 0.4, and both AVE values are also good. While each construct Cronbach's alpha and composite reliability values are >0.7, both measures of reliability, item reliability, and construct reliability are established. For the validity of the data, there are also two measures used one is convergent validity, and the other is discriminant validity. For convergent validity, AVE values are used. The threshold value for the AVE is 0.5 (Hair and Alamer, 2022). From the table of reliability and validity, all the constructs have AVE values >0.5, indicating that all the constructs are convergently valid.

Results and discussion

Demographic profile of the respondents

Table 7 shows the demographic characteristics of the respondents. Among 285 respondents, 164 (75.5%) are male, while 121 (42.5%) are female. The data was collected from different universities in China and Pakistan. The table shows that 142 (50.2%) are Chinese students, and 141 (49.8%) are Pakistani students. The age group section shows that the students are divided into three age groups, <20 years, 20-25 years, and 26 years and above. Most students belong to the age group 20-25 years, which is 140 (49.1%), while 26 (9.1%) are <20 years old and 119 (41.8%) are 26 years and above. The fourth and last section of the table shows the program of the student's studies. According to this, 149 (52.3%) students are undergraduates, 119 (41.8%) are graduates, and 17 (6%) are post-graduates.

Structural model

The structural model explains the relationships among study variables. The proposed structural model is exhibited in Fig. 2.

Regression analysis

Table 8 shows the total direct relationships in the model. The first direct relationship is between artificial intelligence to loss in human decision-making, with a beta value of 0.277. The beta value shows that one unit increase in artificial intelligence will lose human decision-making by 0.277 units among university students in Pakistan and China. This relationship having the t value of 5.040, greater than the threshold value of 1.96, and a p-value of 0.000, <0.05, shows that the relationship is statistically significant. The second relationship is between artificial intelligence the human laziness. The beta value for this relationship is 0.689, which shows that one unit increase in artificial intelligence will make the students of Pakistan and China universities lazy by 0.689 units. The t-value for the relationship is 23.257, which is greater than the threshold value of 1.96, and a p-value of 0.000, which is smaller than the threshold value of 0.05, which shows that this relationship is also statistically significant. The third and last relationship is from artificial intelligence to security and privacy issues of Pakistani and Chinese university students. The beta value for this relationship is 0.686, which shows that a one-unit increase in artificial intelligence will increase security and privacy issues by 0.686. The t-value for the relationship is 17.105, which is greater than the threshold value of 1.96, and the p-value is 0.000, which is smaller than a threshold value of 0.05, indicating that this relationship is also statistically significant.

Hypothesis testing

Table 8 also indicates that the results support all three hypotheses.

Model fitness

Once the reliability and validity of the measurement model are confirmed, the structural model fitness must be assessed in the next step. For the model fitness, several measures are available in the SmartPLS, like SRMR, Chi-square, NFI, etc., but most of the researcher recommends the SRMR for the model fitness in the PLS-SEM. When applying PLS-SEM, a value <0.08 is generally considered a good fit (Hu and Bentler, 1998). However, the table of model fitness shows that the SRMR value is 0.06, which is less than the threshold value of 0.08, which indicates that the model is fit.

Predictive relevance of the model

Table 9 shows the model's prediction power, as we know that the model has total dependent variables. Then there are three predictive values for the model for each variable. The threshold value for predicting the model power is greater than zero. However, Q ² values of 0.02, 0.15, and 0.35, respectively, indicate that an independent variable of the model has a low, moderate, or high predictive relevance for a certain endogenous construct (Hair et al., 2013). Human laziness has the highest predictive relevance, with a Q ² value of 0.338, which shows a moderate effect. Safety and security issues have the second largest predictive relevance with the Q ² value of 0.314, which also show a moderate effect. The last and smallest predictive relevance in decision-making with a Q ² value of 0.033 which shows a low effect. A greater Q ² value shows that the variable or model has the highest prediction power.

Importance performance matrix analysis (IPMA)

Table 10 Multi-group (analysis of gender)a.

Multi-group analysis (MGA)

Multigroup analysis is a technique in structural equation modeling that compares the effects of two classes of categorical variables on the model's relationships. The first category is gender, composed of male and female subgroups or types. Table 10 shows the gender comparison for all three relationships. The data record shows that there were 164 males and 121 females. The p-values of all three relationships are >0.05, which shows that gender is not moderate in any of the relationships. Table 10 shows the country-wise comparison for all three relationships in the model. The p-values of all three relationships are >0.05, indicating no moderating effect of the country on all three relationships. The data records show 143 Pakistanis and 142 Chinese based on the country's origin.

Discussion

AI is becoming an increasingly important element of our lives, with its impact felt in various aspects of our daily life. Like any other technological advancement, there are both benefits and challenges. This study examined the association of AI with human loss in decision-making, laziness and safety and privacy concerns. The results given Tables 11 and 12 show that AI has a significant positive relationship with all these variables. The findings of this study also support that the use of AI technologies is creating problems for users related to security and privacy. Previous research has also shown similar results (Bartoletti, 2019; Saura et al., 2022; Bartneck et al., 2021). Using AI technology in an educational organization also leads to security and privacy issues for students, teachers, and institutions. In today's information age, security and privacy are critical concerns of AI technology use in educational organizations (Kamenskih, 2022). Skills specific to using AI technology are required for its effective use. Insufficient knowledge about the use will lead to security and privacy issues (Vazhayil and Shetty, 2019). Mostly, educational firms do not have AI technology experts in managing it, which again increases its vulnerability in the context of security and privacy issues. Even if its users have sound skills and the firms have experienced AI managers, no one can deny that any security or privacy control could be broken by mistake and could lead to serious security and privacy problems. Moreover, the fact that people with different levels of skills and competence interact in educational organizations also leads to the hacking or leaking of personal and institutional data (Kamenskih, 2022). AI is based on algorithms and uses large data sets to automate instruction (Araujo et al., 2020). Any mistake in the algorithms will create serious problems, and unlike humans, it will repeat the same mistake in making its own decisions. It also increases the threat to institutional and student data security and privacy. The same challenge is coming from the student end. They can be easily victimized as they are not excellently trained to use AI (Asaro, 2019). With the increase in the number of users, competence division and distance, safety and privacy concerns increase (Lv and Singh, 2020). The consequences depend upon the nature of the attack and the data been leaked or used by the attackers (Vassileva, 2008).

The findings also show that the access use of AI will gradually lead to the loss of human decision-making power. The results also endorsed the statement that AI is one of the major causes of the human loss of decision-making power. Several researchers from the past have also found that AI is a major cause responsible for the gradual loss of people's decision-making (Pomerol, 1997; Duan et al., 2019; Cukurova et al., 2019). AI performs repetitive tasks in an automated manner and does not let humans memorize, use analytical mind skills, or use cognition, leading to the loss of decision-making capabilities (Nikita, 2023). An online environment for education can be a good option (VanLangen, 2021), but the classroom's physical environment is the prioritized education mode (Dib and Adamo, 2014). In a real environment, there is a significant level of interaction between the teacher and students, which develop the character and civic bases of the students, e.g., students can learn from other students, ask teachers questions, and even feel the education environment. Along with the curriculum, they can learn and adopt many positive understandings (Quinlan et al., 2014). They can learn to use their cognitive power to choose options, etc. But unfortunately, the use of AI technology minimizes the real-time physical interaction (Mantello et al., 2021) and the education environment between students and teachers, which has a considerable impact on students' schooling, character, civic responsibility, and their power to make decisions, i.e., use their cognition. AI technology reduces the cognitive power of humans who make their own decisions (Hassani and Unger, 2020).

AI technology has undoubtedly transformed or at least affected many fields (IEEE, 2019; Al-Ansi and Al-Ansi, 2023). Its applications have been developed for the benefit of humankind (Justin and Mizuko, 2017). As technology assists employees in many ways, they must be aware of the pros and cons of the technology and must know its applications in a particular field (Nadir et al., 2012). Technology and humans are closely connected; the success of one is strongly dependent on the other; therefore, there is a need to ensure the acceptance of technology for human welfare (Ho et al., 2022). Many researchers have discussed the user's perception of a technology (Vazhayil and Shetty, 2019), and many have emphasized its legislative and regulatory issues (Khan et al., 2014). Therefore, careful selection is necessary to adopt or implement any technology (Ahmad and Shahid, 2015). Once imagined in films, AI now runs a significant portion of the technology, i.e., health, transport, space, and business. As AI enters the education sector, it has been affected to a greater extent (Hübner, 2021). AI further strengthened its role in education, especially during the recent COVID-19 pandemic, and invaded the traditional way of teaching by providing many opportunities to educational institutions, teachers, and students to continue their educational processes (Štrbo, 2020; Al-Ansi, 2022; Akram et al., 2021). AI applications/technology like chatbots, virtual reality, personalized learning systems, social robots, tutoring systems, etc., assist the educational environment in facing modern-day challenges and shape education and learning processes (Schiff, 2021). In addition, it is also helping with administrative tasks like admission, grading, curriculum setting, and record-keeping, to name a few (Andreotta and Kirkham, 2021). It can be said that AI is likely to affect, enter and shape the educational process on both the institutional and student sides to a greater extent (Xie et al., 2021). This phenomenon hosts some questions regarding the ethical concerns of AI technology, its implementation, and its impact on universities, teachers, and students.

There may be many reasons for these controversial findings, but the cultural factor was considered one of the main factors (Elliott, 2019). According to researchers, people with high cultural values have not adopted the AI problem, so this cultural constraint remains a barrier for the AI to influence their behaviors (Di Vaio et al., 2020; Mantelero, 2018). The other thing is that privacy is a term that has a different meaning from culture to culture (Ho et al., 2022). In some cultures, people consider minimal interference in personal life a big privacy issue, while in some cultures, people even ignore these types of things (Mantello et al., 2021). The results are similar to Zhang et al. ( 2022), Aiken and Epstein ( 2000), and Bhbosale et al. ( 2020), which focus on the ethical issues of AI in education. These studies show that AI use in education is the reason for laziness among students and teachers. In short, the researchers are divided on the AI concerns in education, just like in other sectors. But they agree on the positive role AI plays in education. AI in education leads to laziness, loss of decision-making capabilities, and security or privacy issues. But all these issues can be minimized if AI is properly implemented, managed, and used in education.

Implications

The research has important implications for technology developers, the organization that adopts the technology, and the policymakers. The study highlights the importance of addressing ethical concerns during AI technology's development and implementation stage. It also provides guidelines for the government and policymakers regarding the issues arising with AI technology and its implementation in any organization, especially in education. AI can revolutionize the education sector, but it has some potential drawbacks. Implications suggest that we must be aware of the possible impact of AI on laziness, decision-making, privacy, and security and that we should design AI systems that have a very minimal impact.

Managerial Implications

Those associated with the development and use of AI technology in education need to find out the advantages and challenges of AI in this sector and balance these advantages with the challenges of laziness, decision-making, and privacy or security while protecting human creativity and intuition. AI systems should be designed to be transparent and ethical in all manners. Educational organizations should use AI technology to assist their teachers in their routine activities, not to replace them.

Theoretical Implications

A loss of human decision-making capacity is one of the implications of AI in education. Since AI systems are capable of processing enormous amounts of data and producing precise predictions, there is a risk that humans would become overly dependent on AI in making decisions. This may reduce critical thinking and innovation for both students and teachers, which could lower the standard of education. Educators should be aware of how AI influences decision-making processes and must balance the benefits of AI with human intuition and creativity. AI may potentially affect school security. AI systems can track student behavior, identify potential dangers, and identify situations where children might require more help. There are worries that AI could be applied to unjustly target particular student groups or violate students' privacy. Therefore, educators must be aware of the potential ethical ramifications of AI and design AI systems that prioritize security and privacy for users and educational organizations. AI makes people lazier is another potential impact on education. Teachers and learners may become more dependent on AI systems and lose interest in performing activities or learning new skills or methodologies. This might lead to a decline in educational quality and a lack of personal development among people. Therefore, teachers must be aware of the possible detrimental impacts of AI on learners' motivation and should create educational environments that motivate them to participate actively in getting an education.

Conclusion

AI can significantly affect the education sector. Though it benefits education and assists in many academic and administrative tasks, its concerns about the loss of decision-making, laziness, and security may not be ignored. It supports decision-making, helps teachers and students perform various tasks, and automates many processes. Slowly and gradually, AI adoption and dependency in the education sector are increasing, which invites these challenges. The results show that using AI in education increases the loss of human decision-making capabilities, makes users lazy by performing and automating the work, and increases security and privacy issues.

Recommendations

1. 1.
The designer's foremost priority should be ensuring that AI will not cause any ethical concerns in education. Realistically, it is impossible, but at least severe ethical problems (both individual and societal) can be minimized during this phase.

2. 2.
AI technology and applications in education need to be backed by solid and secure algorithms that ensure the technology's security, privacy, and users.

3. 3.
Bias behavior of AI must be minimized, and issues of loss of human decision-making and laziness must be addressed.

4. 4.
Dependency on AI technology in decision-making must be reduced to a certain level to protect human cognition.

5. 5.
Teachers and students should be given training before using AI technology.

Future work

1. 1.
Research can be conducted to study the other concerns of AI in education which were not studied.

2. 2.
Description and enumeration of the documents under analysis.

3. 3.
Procedure for the analysis of documents. Discourse analysis and categorization.

4. 4.
Similar studies can be conducted in other geographic areas and countries.

Limitations

This study is limited to three basic ethical concerns of AI: loss of decision-making, human laziness, and privacy and security. Several other ethical concerns need to be studied. Other research methodologies can be adopted to make it more general.

Acknowledgements

The research is funded by the Universiti Kuala Lumpur, Kuala Lumpur 50250, Malaysia, under the agreement, UniKL/Cori/iccr/04/21.

Author information

Authors and Affiliations

1. Institute of Business Management, Karachi, Pakistan

Sayed Fayaz Ahmad

2. Sejong University, Seoul, Korea

Heesup Han

3. Riphah International University, Islamabad, Pakistan

Muhammad Mansoor Alam

4. Universiti Kuala Lumpur, Kuala Lumpur, Malaysia

Mohd. Khairul Rehmat

5. University of Gwadar, Gwadar, Pakistan

Muhammad Irshad

6. Universidad de Playa Ancha, Valparaiso, Chile

Marcelo Arraño-Muñoz

7. Universidad Loyola Andalucía, Córdoba, Spain

Antonio Ariza-Montes

Contributions

The authors confirm their contribution to the paper as follows: Introduction: SFA and HH; Materials: MMA and MKH, MI; Methods: MI and SFA; Data collection: SFA, MMA, MKR, MI, MMA, and AAM; Data analysis and interpretation SFA, HH, MI, and AAM; Draft preparation: MMA, MKR, MI, MMA, and AAM; Writing and review: SFA, HH, MMA, MKR, MI, MMA, and AAM. All authors read, edited, and finalized the manuscript.

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Correspondence to Sayed Fayaz Ahmad or Heesup Han.

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The authors declare no competing interests.

Ethical approval

The evaluation survey questionnaire and methodology were examined, approved, and endorsed by the research ethics committee of the University of Gwadar on 1 March 2021 (see supplementary information). The study meets the requirements of the National Statement on Ethical Conduct in Human Research (2007). The procedures used in this study adhere to the tents of the declaration of Helsinki.

Informed consent

Informed consent was obtained from all participants before the data was collected. We informed each participant of their rights, the purpose of the study and to safeguard their personal information.

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Ahmad, S.F., Han, H., Alam, M.M. et al. Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanit Soc Sci Commun 10, 311 (2023). https://doi.org/10.1057/s41599-023-01787-8
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Impact of artificial intelligence on human loss in decision making, laziness and safety in education - Humanities and Social Sciences Communications

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This study examines the impact of artificial intelligence (AI) on loss in decision-making, laziness, and privacy concerns among university students in Pakistan and China. Like other sectors, education also adopts AI technologies to address modern-day challenges. AI investment will grow to USD 253.82 million from 2021 to 2025.