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Digital learning and transformation of education

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Open digital learning opportunities for all

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近來，生成式人工智慧（Generative AI，以下簡稱AI）應用快速發展，引起大眾關注。越來越多人工智慧工具，如文字類型的ChatGPT、圖像類型的Midjourney、DALL·E 2等已滲入生活許多面向。AI改變了未來的職業定義，同時為高等教育帶來全新的挑戰；帶來便利性，同時產生諸多待討論的新議題。因此，本指引將隨著科技持續進步，繼續更新，協助本校師生及行政人員於教學、學習與工作上，以謹慎負責的態度善用AI。

生成式人工智慧（Generative AI）是一項快速發展的新興技術，藉由提供大量數據給機器學習模型，從中提取高層次的抽象特徵，並根據特徵生成如文案、圖片、音訊等作品。若能善用AI，將大幅提升學習和工作效率，提供更高品質的教學研究內容，為生活帶來便利。但作為工具，亦有其限制，使用者依然必須針對正確性作嚴謹的把關確認，不宜過度依賴。不同類型AI的應用範圍與面向相當不同，其中基於大型語言模型（LLM）的ChatGPT對教育有特殊影響，故以下將以此技術進行討論。

(二)教學現場導入：於教學現場將ChatGPT定位為「輔助工具」，部分應用列舉如下：

(三 )學習評量：學習評量可能因ChatGPT而有巨大的轉變，部分應用列舉如下：

(一)語言翻譯：ChatGPT為大型語言模型，能協助語言翻譯。惟仍需親自檢視翻譯用字、文法及語意等，確保翻譯成品之正確性。

(二)縮短作業期程：可善用ChatGPT快速彙整龐大的資料數據及重複性高的事務，例如：會議紀錄撰寫、資料文章重點整理等，提高工作效率。

(三)撰寫計畫、文案及Email書信：輸入文本架構、目標或期望產出的需求，ChatGPT可提供參考資料並產出文案初稿，再由行政人員依據專業知識修調內容。若已有初稿，也可利用ChatGPT協助修正或潤飾文字。

ChatGPT藉由大量現有數據進行學習，根據機率與特徵向量，依序生成回覆。然其內容可能含有錯誤訊息，甚至是基本的事實型錯誤，如計算錯誤等。故使用ChatGPT時，除了學習如何精準提問外，使用者亦須積極主動判斷內容正確性，不能將生成結果直接作為報告、評量等成果。

師生應秉持誠實、精確、公平、客觀之精神自我監督，謹守學術誠信，妥善保存研究紀錄與原始資料，並遵守政府機關對研究相關之法令與倫理行為準則，不應有造假、變造、抄襲、請他人代寫或以翻譯代替論著等行為。

由於輸入至ChatGPT的內容可能會被紀錄並加以學習應用，故使用時須謹慎確認，非必要時請勿將未公開文件、個人訊息等資料輸入至對話框內。

適當運用ChatGPT能大幅提高工作效率，惟仍需注意避免過度依賴，其應定位為輔助工具，所生成的文本僅供參考，使用者在減輕撰寫負擔時，應專注於把關內容的品質與正確性，並以批判閱讀（Critical Reading）的角度檢視生成文本，為最終內容負責。

肆、面對人工智慧世代來臨該有的思維與態度

因應AI技術發展的日新月異，我們很難預料未來科技變化會是以何種形式、在何時如何發生。如同歷史上曾發生過的數次工業革命，科技發展是整合現有資源，以滿足人們的各種需求，有助人們在面對複雜工作或任務時，能運用各項AI技術提升效率，帶來更高層次的文明發展。

禁止使用AI無法符應時代趨勢與解決問題，亦無法協助師生認識、善用這個強大的數位工具，優化日常專業與學習，因此，建議師生均能以客觀開放的態度，經過良好的溝通，達成運用AI工具之共識，進而擬訂課室使用AI之規範，提升教學品質，同時增進學習成效。學生除瞭解AI為何外，更期望透過教師的引導與規範，正確運用AI獲得多元新知，並內化為個人的創見與反思，共同營造AI融入校園的環境。

以下彙整相關網路資源，提供師生及行政人員善加參考利用。

2. Adapting Your Teaching to AI Generator Tools (5 Strategies for Adapting Your Assessments to AI)

3. ChatGPT Prompts for Teachers: Unlocking the Potential of AI in Education

5. How to use Chat GPT: Opportunities and Risks for Researchers

6. ChatGPT Tutorial: How to Use Chat GPT For Beginners 2023 （Youtube）

7. Complete ChatGPT Tutorial （Youtube）

8. 文字AI超簡單！ChatGPT全方位必修課 報告 論文 New Bing Notion AI (Youtube)

[1] Bawn, Kathy & James Bisley. (2023, March 27). Teaching Guidance for ChatGPT and Related AI Developments. University of California, Los Angeles.
https://ucla.app.box.com/file/1173334105138?s=iqs7q59vvw21i30masvxv7l90st772uo

[2] Eberly Center. (2023, February 2). AI Tools (ChatGPT) FAQ. Carnegie Mellon University.
https://www.cmu.edu/teaching/technology/aitools/index.html

[3] Kunihiro, Ohta. (2023, April 3). 生成系AI(ChatGPT, BingAI, Bard, Midjourney, Stable Diffusion等)について [About generative AI (ChatGPT, BingAI, Bard, Midjourney, Stable Diffusion, etc.]. The University of Tokyo.
https://utelecon.adm.u-tokyo.ac.jp/docs/20230403-generative-ai

[4]Suga, Koichi. (2023, April 21). About the Use of Generative Artificial Intelligence (ChatGPT,etc.). Waseda University.
https://www.waseda.jp/top/en/news/77786

※本指引由本校教務處諮詢校內專家教師後，與ChatGPT協作潤飾後擬訂。

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國立臺灣師範大學 - 生成式AI之學習應用及參考指引 - 國立臺灣師範大學教學發展中心

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近來，生成式人工智慧（Generative AI，以下簡稱AI）應用快速發展，引起大眾關注。越來越多人工智慧工具，如文字類型的ChatGPT、圖像類型的Midjourney、DALL·E ...

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Communities of Practice

By joining you have the opportunity to raise the issues that concern you, voice your opinion in discussions, participate in working groups, exchange materials and experience, access good practices, learn from peers, share resources and be informed about the latest developments concerning the CoP topic. Communities of Practice are open to everyone, individuals and organizations alike.

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By joining you have the opportunity to raise the issues that concern you, voice your opinion in discussions, participate in working groups, exchange materials and experience, access good practices, learn from peers, share resources and be informed about the latest developments concerning the CoP topic.

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Writing enriches life, shaping our business, academic, and personal narratives. Yet, as AI redefines boundaries, it poses compelling questions: Can it replace human writers, or should it merely enhance our creative processes? How do we harness AI in our writing workflows effectively? We explore these questions and more in this guide on how to write using AI.

Introduction to AI Writing Tools

If your goal is to write better, you probably realize artificial intelligence has a role to play. The tricky part is understanding how (and to what extent) you should use AI for writing.

Well, the intersection of your writing projects with AI starts with the right AI tools-and there are many.

Let's narrow down the field to find a correct fit. Here are some general AI writing tools for you to consider. We'll reference each of these throughout our guide, but you only need one to start with.

All-in-one AI Writing Platform: AI writing platforms offer specialized features specifically for writing. They have easy-to-use writing templates for various types of writing and long-form document editors for creating longer works. For this, we recommend either Writesonic (read review) or Rytr (read Rytr review).

An AI Chatbot: Chatbots are one of the most popular AI types to use. They're flexible since you can interact with them through conversational language. We suggest ChatGPT or Chatsonic as good starter AI chatbots.

Business and Grammar-Focused AI: If most of your writing is done in the workplace (and you're not in marketing), we recommend an AI-powered grammar checker. Perhaps the most popular is Grammarly. But it does more than just check grammar and syntax. It also has an integrated AI writer that is very good for business email and other communication. We highly recommend Grammarly to any writer.

There are other AI writers that leverage generative AI well, and we'd highly encourage you to look into them. At the end of this article, we've listed them by use-case for those needing specific tools for various writing types (e.g., resumes, books, emails, etc.)

Benefits of Writing with AI

You'll notice a staggering amount of benefits as you explore how AI can fit into your writing and creation workflows. These include:

1. Better Research: AI enhances your ability to dive deep into topics. Your writing will benefit from more knowledge sources and reasoned explanations from AI research.

2. Spend Time on What Matters: Artificial intelligence takes care of repetitive tasks, enabling you to concentrate on the more imaginative elements. We'll take efficiencies anywhere we can.

3. Versatile Across Different Types of Writing: AI tools are useful across various writing formats, from blogs to business documents and even books.

4. Makes Writing Available to More People: AI democratizes writing and encourages a broader demographic to start drafting their ideas. If you've struggled with writing, AI might just be the catalyst that you need.

When used correctly, AI writing assistants are perfectly safe to use. Using AI throughout your writing process will save time and enrich your work.

Cons of Using AI in Writing

However, challenges like content inflation, detection of AI-generated text, and ethical concerns about originality and plagiarism loom large. The key is using AI as an assistant rather than replacing human creativity and judgment. Using AI poorly has significant downsides:

Content Inflation: AI is like the printing press. It makes it so that more work gets published every day. Unfortunately, AI-created work (as opposed to human-created work) is spreading rapidly. This makes content more plentiful but lowers its quality, leaving readers in the lurch.

Plagiarism Concerns: Most current AI models are trained on copyrighted material. This means that it is very possible that fragments of copyrighted works can appear in the outputs of generative AI tools. Simply copying and pasting that information could land you in plagiarism trouble.

Loss of Reputation: Some AIs, like ChatGPT, have a very distinctive tone. Readers have developed the ability to quickly sniff low-quality work generated by these tools. If you are trying to publish something that matters for your business, relying too heavily on the words that AI creates can tank your reputation and authority.

Case Study of Negative AI Use

This can be seen in a very narrow domain, SEO. This happened with the March 2024 Core Update from Google. Many low-quality publishers and content mills spent most of 2023 creating millions of pages of AI content. And it ranked well-for a while. In March 2024, Google penalized hundreds of websites for low-quality and spam content, most of which was generated via AI tools. Websites went from millions of monthly visitors to just a few thousand overnight. AI content was a short-term play with long-term consequences.

AI writing in other industries (published books, paid email newsletters, etc.) can have its own negative consequences that aren't worth it. The rest of this guide goes into showing you exactly how to use AI to write while avoiding negative repercussions. There are ways to use it to help you without turning into a huge liability later on.

A Guide to Writing with AI (Step-by-step)

Here's a step-by-step approach to infusing your writing process with AI. We have tips and tricks but also some warnings for overusing AI.

1. Conduct Topic Research with AI

Research is a foundational part of writing high-quality content. When something is published, someone's reputation is at stake. Research makes sure that statements, claims, and opinions are backed up to a reasonable degree. Obviously, it's big for academic and business writing. But it's also good for other formats as well.

For general writing, like blog articles and thought leadership, Search Engine-based research is wonderful. You can use your preferred AI chatbot, but some good options are Google's Gemini, Microsoft's Copilot, and Perplexity.ai. These are great for finding facts and figures and coming up with new ideas.

For academic writing, we like to use Consensus.app, an AI journal finder. It digs through academic journals to find relevant articles based on your search term and topic. Its AI also gives a one-sentence summary of the paper and does other cool things to help you find scholarly information quickly.

Less formal writing might need less research, but in our opinion, there's always something to dig into. Marketing teams often spend a lot of time on their GTM (go-to-market) strategy, which means they do a lot of competitor research. Yes, AI is making that easy, too. Predis.ai is one such tool that helps monitor competitor social profiles. Competitor analysis is a vital part of research for a lot of writing and shouldn't be shrugged off. Similar research can be done for SEO with AI tools for SEO.

Unless you're going down unnecessary rabbit holes, research always makes a difference in written works. This goes for books, academic articles, blog posts, and social media posts.

2. Make an Outline Using AI

We find that an outline makes all the difference for writing projects that are more than a couple hundred words. This is because they keep an author on track. It's just too easy for a writer to get carried away and veer off from the main point.

You should start with your main idea for your outline or content brief. What's the big takeaway? In academic writing, this is the thesis statement, but it's just as important as in other types of writing.

Your outline will start to take shape as you add supporting ideas. If you're making an agreement or providing a hot-take, your outline should show the steps about how you reached that conclusion. If you're informing your audience about a process or general piece of knowledge, your outline will reflect the questions that readers often have concerning it. Fiction outlines show the overall plot and development of your story.

AI chatbots are excellent at helping with this because of their conversational nature. Ask one to help you structure your thoughts. Then, ask it to provide other ideas or incorporate any additional thoughts you have. The back and forth is great for creating something you can work with.

3. Write First and Subsequent Drafts

Start by setting the foundation of your piece. Since you already have a written outline, you should try expanding it at this point. You may explore using AI-infused document editors (like those found with Writesonic or WordHero) to help speed up your first draft quickly.

We'd recommend that you don't use AI tools that will generate an entire draft at once. These can go in a different direction than you expected, are usually more basic, and need a lot of editing. You are better off using AI on a line-by-line basis to have the most control. Many AI writers also come with Chrome and desktop extensions, so you can bring generative AI to the tools you already use to write.

Any way you go about it should be about speed and getting a broad version of your ideas on paper. Don't stress about being perfect. Think of it as a rough draft that you can improve and make better.

Work Through Drafts Until You're Finished

You'll certainly run into writer's block, further questions to research, and other things that slow your writing down. To pick it back up, treat AI like a collaborator. Ask specific questions to help you focus. This helps AI give you useful and interesting information that moves you forward. It can improve your work by bringing in new ideas or facts you haven't considered.

However, reflect on the balance between AI-generated content and your own writing. While the exact ratio will vary based on your project and personal preference, ensuring your voice dominates is a good rule. AI writing often sounds robotic and unnatural, and it often includes fluff words that detract from your work. At its worst, it can riddle your writing with generic and repeated phrases.

Make sure your drafts accurately represent your thoughts and opinions. Liberally make revisions, subtractions, and additions to any content generated by AI. Be cautious when considering sentences from AI, as the temptation to use them "as-is" can be strong. Original writing is often the best and safest.

Remember, if an AI writing tool writes you something, it can easily do something similar for another person. And if you publish something in your name (or your company's), you are responsible for it-even if AI generated it for you.

4. Create Rich Content Using AI

Creating rich content for written works is a surefire way to make your message more accessible and memorable. These elements let readers understand and digest your information in multiple ways, meaning they'll understand better what you tried to communicate. This works for fiction literature as much as it does for business blogs. You just need to know what AI can do for you when creating multimedia assets for your written works.

Enriching Content with Multimedia

AI makes it easier to include multimedia in your writing. With AI image generation tools, you can create custom visuals that align perfectly with your text, making your articles, newsletters, or business presentations more engaging. Canva's AI helps make custom images and infographics. Using premade image templates alongside advanced generative AI makes these assets very easy to create. There are dozens of AI image generators that can create unique images.

AI can also help create videos and edit audio for podcasts. Both of these mediums are great avenues for disseminating your message.

AI for Generating FAQs, Schemas, and Other SEO Essentials

AI can assist in generating comprehensive FAQs relevant to your content and addressing common questions your audience might have. This adds value to your content and improves its visibility in search engines, especially in Google's People Also Ask (PAA), Search Generative Experience (SGE), and rich snippets.

Similarly, AI tools can generate schema markup for your content, making it more structured and easily interpretable by search engines. Feed an AI chatbot your content and ask what kind of generally acceptable schema it can create. Have it generate the JSON-LD for that content for any appropriate entity found in your content or web page.

AI tools for SEO help ensure that search engines can easily discover your content. From generating keyword-rich titles and meta descriptions to optimizing image alt attributes, AI can automate the tedious aspects of SEO, making your content more likely to rank higher in search results.

These optimization techniques can also be applied to academic writing since articles can be discovered by searching academic databases. Direct keyword and topical matching is more important for these less sophisticated search engines.

5. Substantive and Copy Editing with AI

Ensuring the accuracy and polish of your content is as crucial as the initial creation. Substantive editing and copy editing are essential steps where AI can significantly enhance efficiency.

Substantive editing looks into the structure, content, and flow of your writing. This is often the most brutal of editing, and you tend to see a lot of "red ink." AI tools can assist by suggesting areas where arguments may lack support and proposing enhancements. For example, AI chatbots can be asked to analyze your text for logical flow and suggest reordering sections for better narrative progression. This may help ensure that each part of your content contributes meaningfully to the whole.

Guard against plagiarism by using tools like Copyscape or Grammarly's plagiarism checker. It's essential to verify that the AI's contributions are original and, when necessary, properly cite sources. This step guarantees the integrity of your work and protects your credibility as an author.

When it comes to copy editing, AI shines by offering advanced grammar, punctuation, and spelling checks beyond what traditional word processors can achieve. Tools like Grammarly and the Hemingway App further assess sentence structure, offer synonyms to avoid repetition, and suggest simpler alternatives to complex phrases. This polishes your writing and improves readability, making your content more accessible to a wider audience.

The Hemingway App highlights sentences that are hard to read, passive voice usage, and adverbs that could be replaced, pushing you towards crisp, clear, and active writing.

Different Use Cases for AI Writing

Writing is important in multiple industries and areas of life. Various types of writing use different methods, structures, and rules. Hence, there is a need for different AI tools.

How to Use AI for Writing Emails

There are many AI email writing tools you can use to streamline your email writing process. Whether marketing, sales, or general communication, emails are a medium all their own. They often have to be very brief and highly engaging. Most emails go unread, so to stand out, subject lines, pre-headings, and the whole body have to be engaging. This includes images as well.

AI Email Writers like Hoppy Copy are uniquely positioned to help with this kind of writing. Trained using successful marketing and sales emails, Hoppy Copy understands the cadence.

How to Write Resumes Using AI

Resume writing is very specific. They are full of action verbs with a results orientation. AI can help you best position yourself to potential employers. AI Resume builders like KickResume are trained on successful resume examples to help you write and design your resume to target specific jobs and stand out from the other candidates.

How to Write Essays and Academic Works With AI

We all know the challenges of essay and academic writing. There is a strong need for formal speech and logical coherence between sentences. There are powerful AI Essay Writing tools you can use, like Jasper or Rytr, to streamline the entire writing process, including research, formatting, and editing.

However, Grammarly provides one of the best toolsets for the job. Since most academic writing is written by hand, Grammarly shores up the grammar, syntax, and readability of writing. You can also set goals for each piece of writing to make sure to strike the right tone.

How AI can Help You Write a Book

Stories are obviously a unique type of writing. But AI can help create engaging dialogue, move a plot along, and build worlds that readers want to embody. If you're a fiction and story writer, Sudowrite is just the AI story writer for you. It helps you jot down your ideas and direction, set your genre, and create characters. Its paid version does a pretty good job of retaining these details to make a consistent story from chapter to chapter.

Get Started On Your Next Writing Project

There's nothing like the satisfaction of finishing up a writing project and sending it off to readers (we say as we wrap this article up). There is a lot of satisfaction in nurturing an idea to life. From conception to publishing, writing has its many challenges.

Thankfully, AI tools can insert themselves effortlessly at various parts of the writing process. No matter what you choose to write, AI can help you achieve your dreams and writing goals.

We've covered a good many tools and how they fit into various writing processes. If you need more recommendations, we have them. Check out our articles on the best AI writers, AI rewriting software, and AI email creators. You should also see our guides on finding the best AI content detectors and grammar checkers.

Answers to more questions on the intersection of writing and AI.

Can AI write books?

Can I use AI for writing essays?

How can I use AI to write resumes and cover letters?

Can I write emails with AI?

How do you use AI to write blog posts?

What is content inflation and how does AI contribute to it?

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How to Write With AI: Essential Guide, Tools, & Tips (2024)

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Learn how to write better with AI. This writing guide includes steps, tools, and a case study. Learn to write books, resumes, essays, & more.

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AI's Role in the Education Revolution

AI's Role in the Education Revolution

AI is transforming education.

We want to ensure that the changes benefit pupils, teachers and society.
‍
By drawing together a large community of teachers and leaders from all school sectors and settings, we will support schools and colleges to maximise the benefits of AI and minimise the risks.

Our Mission

We aim to ensure that AI will maximise the interests of all involved in education, students, teachers, support staff, parents, governors and the wider community.

We want to do so by ensuring that AI enhances human intelligence and experience, and does so in a way that above all is to the benefit of the most vulnerable - including those with the least means, additional learning needs and those from diverse backgrounds.

We are very clear of the risks of AI, which are very real.

Infantilisation of students (and staff)

Moral risk, not least through deep fake

Perceptions about cheating and dishonesty

Lack of responsibility - or answers to the question : who is in charge?

The gains are many, but will only be realised if the risks are understood and addressed:

Personalised formative assessment

Opportunities for AI facilitated VR to enhance student experience of a range of STEM, social science and humanities subjects

Enhanced opportunities for students to learn at their own time, in their own way

Increased efficiency of teachers' preparation, delivery, assessment and reporting

Explore our vast library of insights and resources from industry experts

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AI's Role in the Education Revolution. Discover, Engage, Transform. AI in Education is a cross-sector initiative conceived by Sir Anthony Seldon, who has a long interest in AI. It was announced at a conference on AI in education held at Epsom College on 18 May 2023, and in a letter signed by 60 school leader signatories to The Times on 20 May.

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ShareClass 是一個開放的教學資源共享平台，幫助中小學教師探索、分享，與使用優秀的教育內容

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AI will change the world. Who will change AI?

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Better Sharing, Brighter Future

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Cropped, Open Culture Strategic Workshop by Filipa Alfama, CC BY 4.0

One year after Creative Commons (CC) hosted an exploratory Open Culture Roundtable, in Lisbon, Portugal, which initiated the Towards a Recommendation on Open Culture (TAROC) global initiative, nearly 50 stakeholders from all continents gathered again for a strategic workshop, in Lisbon in May 2024. In this blog post, we share a snapshot of key highlights.

About CC

Open Culture, Open Heritage

About CC, Community

About CC

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Open Culture
Cropped, Open Culture Strategic Workshop by Filipa Alfama, CC BY 4.0

One year after Creative Commons (CC) hosted an exploratory Open Culture Roundtable, in Lisbon, Portugal, which initiated the Towards a Recommendation on Open Culture (TAROC) global initiative, nearly 50 stakeholders from all continents gathered again for a strategic workshop, in Lisbon in May 2024. In this blog post, we share a snapshot of key highlights.

About CC

Open Culture, Open Heritage

About CC, Community

About CC

Images Attribution
view

Le Sortie de l'opéra en l'an 2000 by A. Robida - Library of Congress Prints and Photographs Division - Public Domain.

" An Original Theory or New Hypothesis of the Universe. Plate XXXI. ". By Thomas Wright. Public Domain

" Community Needs Care" by Camila Leão for Fine Acts x OBI is licensed via https://creativecommons.org/licenses/by-nc-sa/4.0/.

CC staff photos are licensed under CC BY 4.0. Gratitude to Sara Jordan Photography.

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Creative Commons is an international nonprofit organization that empowers people to grow and sustain the thriving commons of shared knowledge and culture we need to address the world's most pressing challenges and create a brighter future for all.

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澳 門 特 別 行 政 區

第14/2024號行政法規

修改第15/2014號行政法規

《本地學制正規教育課程框架》

行政長官根據 《澳門特別行政區基本法》第五十條（五）項及 第9/2006號法律 《非高等教育制度綱要法》第二十二條第二款的規定，經徵詢行政會的意見，制定本補充性行政法規。

第一條

修改第15/2014號行政法規

經 第33/2019號行政法規修改的 第15/2014號行政法規 的第九條及第十一條修改如下：

"第九條

學校年度及教育與教學活動時間

一、〔......〕

二、〔......〕

三、〔......〕

四、在相鄰的兩節課之間，須為學生安排不少於五分鐘的休息時間。

五、〔......〕

六、〔......〕

七、〔......〕

第十一條

學校的課程開發

一、〔......〕

二、〔......〕

三、學校應重視加強綜合應用技能教育，尤其以設置跨學習領域或跨學科的綜合性科目、於學年內訂定開展綜合應用技能教育的專題活動時間及在不同科目教學計劃內安排綜合性的主題教學活動時間等方式進行。

四、以上款所指方式進行的綜合應用技能教育的教學活動時間，可分配及計入所涉及的一個或多個科目的教學活動時間。"

第二條

取代附表

第15/2014號行政法規 第九條第七款所指的附表二、附表三及附表四，由作為本行政法規組成部分的附件所載的附表二、附表三及附表四取代。

第三條

生效及產生效力

一、本行政法規自公佈翌日起生效，但不影響以下數款規定的適用。

二、本行政法規自二零二四/二零二五學校年度的首日起在幼兒教育階段各年級、小學教育階段一至三年級產生效力；自二零二五/二零二六學校年度的首日起在小學教育階段四至六年級、初中教育階段各年級產生效力；自二零二六/二零二七學校年度的首日起在高中教育階段各年級產生效力。

三、具備條件自二零二四/二零二五學校年度的首日起在小學教育階段四至六年級、初中教育階段及高中教育階段的各年級實施本行政法規的學校，可實施之。

四、本行政法規按以上兩款的規定產生效力前，根據本行政法規修改前的 第15/2014號行政法規 訂定的幼兒教育階段、小學教育階段、初中教育階段及高中教育階段的課程繼續實施。

二零二四年四月十日制定。

命令公佈。

行政長官 賀一誠

---

附件

（本行政法規第二條所指者）

附表二

（第九條第七款所指者）

小學教育課程計劃表

說明：

1. 教學活動時間不包括每學期末或每段末的考試時間，而每節課最少三十五分鐘，最多四十五分鐘。

2. 如為"中文"，須包括普通話。

3. 如為"中文"，可包括普通話。

4. 開展編程教育及人工智能教育的教學活動時間不得少於一千零四十分鐘。

5."體育與健康"每周教學活動時間不得少於七十分鐘。

6. 學校可在此學習領域設置綜合的"藝術"科目，亦可設置分科的"視覺藝術"及"音樂"科目，且可包括"舞蹈"及"戲劇"科目。

7. 學校可根據其教育理念及辦學特色，以及社會及學生發展的需要，增設本附表所列科目以外的一個或多個科目，尤其是體現課程內容整合及科目間相互滲透的科目；該等科目可涉及本附表內所列的一個或多個學習領域，但不得與已列出的科目相同。

8. 如專門的藝術、文化教育活動、社會實踐活動、營會、運動會、校慶活動、開學禮、結業禮、畢業禮、聯歡活動等；學校可自主決定及安排其各學校年度的其他教育活動時間，但應注意有關安排的合理性，關顧到與教學活動及餘暇活動的時間安排的協調性。

附表三

（第九條第七款所指者）

初中教育課程計劃表

說明：

1. 教學活動時間不包括每學期末或每段末的考試時間，而每節課最少三十五分鐘，最多四十五分鐘。

2. 如為"中文"，須包括普通話。

3. 如為"中文"，可包括普通話。

4. 學校可設置綜合的"自然科學"科目，亦可設置分科的"生物"、"物理"及"化學"等科目。

5. 開展編程教育及人工智能教育的教學活動時間不得少於一千一百六十分鐘。

6."體育與健康"每周教學活動時間不得少於七十分鐘。

7. 學校可在此學習領域增設"舞蹈"及"戲劇"科目，以及訂定其教學活動時間。

8. 學校可根據其教育理念及辦學特色，以及社會及學生發展的需要，增設本附表所列科目以外的一個或多個科目，尤其是體現課程內容整合及科目間相互滲透的科目；該等科目可列為必修或選修科目，且可涉及本附表內所列的一個或多個學習領域，但不得與已列出的科目相同。

9. 如專門的藝術、文化教育活動、社會實踐活動、營會、運動會、校慶活動、開學禮、結業禮、畢業禮、聯歡活動等；學校可自主決定及安排其各學校年度的其他教育活動時間，但應注意有關安排的合理性，關顧到與教學活動及餘暇活動的時間安排的協調性。

附表四

（第九條第七款所指者）

高中教育課程計劃表

說明：

1. 教學活動時間不包括每學期末或每段末的考試時間，而每節課最少三十五分鐘，最多四十五分鐘。

2. 如為"中文"，須包括普通話。

3. 如為"中文"，可包括普通話。

4. 學校可設置綜合的"自然科學"科目，亦可設置相關分科的科目。

5. 開展編程教育及人工智能教育的教學活動時間不得少於一千一百二十分鐘。

6."體育與健康"每周教學活動時間不得少於七十分鐘。

7. 學校可在此學習領域增設"舞蹈"及"戲劇"科目，以及訂定其教學活動時間。

8. 學校可根據其教育理念及辦學特色，以及社會及學生發展的需要，增設本附表所列科目以外的一個或多個科目，尤其是體現課程內容整合及科目間相互滲透的科目；該等科目可涉及本附表內所列的一個或多個學習領域，但不得與已列出的科目相同。

9. 學校可根據學生未來升讀高等教育課程或就業的需要及學生的個人興趣，開設屬於本附表"選修"欄目所列類別或跨類別的科目，供學生選修。

10. 如專門的藝術、文化教育活動、社會實踐活動、營會、運動會、校慶活動、開學禮、結業禮、畢業禮、聯歡活動等；學校可自主決定及安排其各學校年度的其他教育活動時間，但應注意有關安排的合理性，關顧到與教學活動及餘暇活動的時間安排的協調性。

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修改第15/2014號行政法規《本地學制正規教育課程框架》。

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UNESCO : International Forum on AI and the Futures of Education

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面對「生成式人工智慧 (generative artificial intelligence)」的興起，本校採取正面且積極的態度，鼓勵師生與AI協作，在教學與學習上能有更多跨學科與跨領域的合作。特定本辦法，作為師生因應的具體措施之指引。

(本措施為暫擬內容，為使本措施更加完善，將依生成式AI的發展進行滾動式修正)

如果使用AI的回答作為學術報告或論文中的資料，請遵循正確的引用格式，

並確保在參考文獻中列出AI為資料來源。

當使用AI交流敏感信息時，例如個人身份信息或學校機密，請確保通訊和訪問方式是安全和私密的。

如果不確定，請向學校或IT部門尋求建議和協助。

在使用AI之前，請先查看學校的政策和指導方針。教師應註明於課程大綱中，並規範使用 AI 的規則，

尤其是正確引註 AI 的方式。如果有任何疑問或不確定，請與學校的相關部門聯繫，
例如教務處、計網中心、圖書館等。

當教師鼓勵學生使用AI時，請提供相應的使用指導和技術支持，幫助學生了解如何使用AI，

並避免使用AI引起的學術不端。
同時，請確保學生明白AI的使用限制和限定範圍，以及AI的使用可能會影響學習成果和學術評價。

教師需要對AI的內容進行適當的檢查和篩選，以確保其內容符合教學要求和學術規範。

在說明課程要求下，可以同意使用AI協助完成評量內容。

教師須以多元方式呈現學習成果（如：口頭報告、手寫報告、製作影片、情境解題等），並避免使用 AI來評量不相關或不適當的內容。

教師可使用AI進行檢視學習評量題目的適切性，提高命題準確性、多樣性及效率。

並可透過示範，讓學生在課堂實作。

1. 組成跨域教師社群，以跨域共好理念討論生成式AI之運用方式。

2. 辦理微學分、微課程、跨域工作坊等系列性生成式AI學習方案。
3. 鼓勵教學單位開設融入生成式AI之課程。

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國立臺中教育大學教務處課務組

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國立臺中教育大學教務處課務組

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國立臺中教育大學教務處課務組

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Year 5 Teacher, Sythwood Primary School

In a Year 5 class, the integration of an AI-driven co-pilot image creator has transformed English and writing into the students' favourite subjects. By enabling students to visualise their character descriptions from "Hilda and the Troll," the tool has particularly motivated a SEND student, turning disappointment into excitement and fostering a newfound enthusiasm for writing.

In a Year 5 classroom, the teacher has innovatively integrated an AI tool, the co-pilot image creator, into English lessons. This application allows students to bring their written descriptions to life by generating images based on their prompts. The class recently focused on character descriptions from the comic "Hilda and the Troll." A notable success story involves a SEND student who initially was disappointed with the image produced from his description. After a constructive discussion on how to refine his description for clarity, the student revised his work to vividly depict a scene set in the Blue Pine Forest. The resulting image generated by the AI tool exceeded his expectations, igniting a passion for writing and creativity.

The teacher ensures a safe and inclusive environment by having students verbally share their ideas, which are then input into the AI tool by the teacher and displayed for the class. This approach not only adheres to consent and data protection standards but also fosters a collaborative learning atmosphere. The use of AI in this setting has been a catalyst for engagement, particularly for those who previously found writing challenging, and has significantly increased interest in English and creative writing among the students.
Teacher report: "I have been using AI in my class regularly with my year 5 class. We talk about it regularly, but we love to use the co-pilot image creator. Recently, we have been using it to write character descriptions from the comic Hilda and the Troll. One child from my class, who is a SEND student, put his setting description in (I entered it into my laptop) and he was disappointed with the results that came out. He said that he imagined being in the centre of forest and the sun shining down. Unfortunately, I don't have the initial draft, but we discussed what he needed to edit for the reader to see what he wanted us to see.
He changed it to this:

‍"Standing in the middle of the Blue Pine Forest, it is the habitat of mother nature. The trees stand tall as the wind whistles through the leaves. As nature increases, joy is spread. The sun was shining to leave a hint of blue".

‍ We put this into co-pilot, and this is the image that it created (See above).

‍ He was so excited when this image popped up that it encouraged him to write more. A child previously who did not really enjoy writing.

‍ The use of the image creator has motivated students to write, and they are always asking if we can play with AI. English and writing are vastly becoming my pupils favourite subject which I have not said once since I have begun teaching".

‍

The scenario in this case study is genuine and based upon real events and data, however its narration (other than the teacher report) has been crafted by AI to uphold a standardised and clear format for readers.

Key Learning

The case highlights the power of AI in education to inspire creativity and engagement among students. Key learnings include the importance of integrating technology in a manner that supports inclusivity and student agency, the role of teacher facilitation in leveraging AI tools for educational benefits, and the potential of AI to transform traditionally challenging subjects into areas of interest and excitement for students.

Risks

While the use of AI in the classroom offers numerous educational benefits, it is not without risks. These include potential issues related to data privacy and security, the need for careful management of student expectations around AI-generated content, and ensuring equitable access to technology for all students. Additionally, reliance on AI for educational activities must be balanced with traditional teaching methods to cultivate a broad range of skills and competencies.

Related Resources

Prompt Examples for Teaching Part 1

PDF Document
"Using the information on my screen, tell me....."

"Using this document about ........... on my screen,summarise........."
Website
"Using the information on my screen, tell me....."

"Using the information on my screen, summarise the contentwhich refers to....."

"Summarise the main points from the information on myscreen...."

‍

Prompt: Simplifying Expertise - Demystifying Key Terms for Young Minds

‍

Now do this: You are an expert on <topic>

Read 'The Text'.

Extract the key terminology

Write a brief and simple definition for each term. Write the definitions with a reading age of <Insert age>

Put this in a table.

The Text:

<Paste your text here>

‍

Prompt: Transform Your Classroom into an Games Based Learning Arena

‍

You are an expert in educational gamification. Please follow these detailed steps:

Ask me to provide the following information:

The Year Group of the students (e.g. Year 7)

The Subject being taught (e.g. English)

The specific Learning Objectives for the lessons

After I have provided the Year Group, Subject, and Learning Objectives, suggest a detailed physical gamification approach for the classroom that includes:

Classroom transformation using props, posters, decorations

A points-based system for hands-on activities

Physical avatars like badges or wristbands

Activity stations with game themes and names

Levels represented by classroom areas

Physical challenges with tangible materials

Hidden content around the classroom

Visual progress trackers

Tangible rewards like stickers or badges

Leaderboards using chalkboards or whiteboards

Group dynamics by dividing students into teams

Ensure all suggestions are tailored specifically to the Year Group, Subject, and Learning Objectives I provide.

‍

Follow on prompt 1:

Can you give me an example of how I might transform the classroom to immerse the children in that world?

‍

Follow on prompt 2:

Provide the details on how to carry out the activity stations. Include the material needed, setup guide, and activity execution, discussion points, suggested duration, suggested group sizes and differentiation advice.

‍

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"Enhancing Creative Writing with AI: A Year 5 Classroom's Journey" | AI In Education

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In a Year 5 class, the integration of an AI-driven co-pilot image creator has transformed English and writing into the students' favourite subjects. By enabling students to visualise their character descriptions from "Hilda and the Troll," the tool has particularly motivated a SEND student, turning disappointment into excitement and fostering a newfound enthusiasm for writing.

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Explore how artificial intelligence (AI) tools can support and enhance the reading process.

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Welcome to NoteLab English

Revolutionise your English teaching with NoteLab English, your brand new lesson planning assistant. Save time, and create fresh, tailored, ready-to-use plans, based on the latest pedagogical thinking. Designed by teachers, for teachers, it's free and just takes a few clicks!

Our intuitive new tool harnesses the power of AI to create unique, relevant, creative and engaging lessons to save you time. You no longer have to spend hours and hours researching and organising supplementary material, but can generate the exact content you need for your own teaching requirements, be it large groups, individuals, beginners or advanced students.

# data[].attributes.attachment_link.description
Revolutionise your English teaching with NoteLab English, your brand new lesson planning assistant. Save time, and create fresh, tailored, ready-to-use plans, based on the latest pedagogical thinking. Designed by teachers, for teachers, it's free and just takes a few clicks!

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Junyi Labs｜教育科技創新實驗室 - 均一教育平台

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Junyi LABs ／ 學習・未來 Future Education Forward! 人工智慧 x 教育科 [...]

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Poe lets you ask questions, get instant answers, and have back-and-forth conversations with AI. Gives access to GPT-4, gpt-3.5-turbo, Claude from Anthropic, and a variety of other bots.

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SPEAK your languages for FREE with the 1st AI-based language teacher 👩‍🎤

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Microsoft Copilot is your companion to inform, entertain and inspire. Get advice, feedback and straightforward answers. Try Copilot now.

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面對「生成式人工智慧 (generative artificial intelligence)」的興起，本校採取正面且積極的態度，鼓勵師生與AI協作，在教學與學習上能有更多跨學科與跨領域的合作。特定本辦法，作為師生因應的具體措施之指引。

(本措施為暫擬內容，為使本措施更加完善，將依生成式AI的發展進行滾動式修正)

如果使用AI的回答作為學術報告或論文中的資料，請遵循正確的引用格式，

並確保在參考文獻中列出AI為資料來源。

當使用AI交流敏感信息時，例如個人身份信息或學校機密，請確保通訊和訪問方式是安全和私密的。

如果不確定，請向學校或IT部門尋求建議和協助。

在使用AI之前，請先查看學校的政策和指導方針。教師應註明於課程大綱中，並規範使用 AI 的規則，

尤其是正確引註 AI 的方式。如果有任何疑問或不確定，請與學校的相關部門聯繫，
例如教務處、計網中心、圖書館等。

當教師鼓勵學生使用AI時，請提供相應的使用指導和技術支持，幫助學生了解如何使用AI，

並避免使用AI引起的學術不端。
同時，請確保學生明白AI的使用限制和限定範圍，以及AI的使用可能會影響學習成果和學術評價。

教師需要對AI的內容進行適當的檢查和篩選，以確保其內容符合教學要求和學術規範。

在說明課程要求下，可以同意使用AI協助完成評量內容。

教師須以多元方式呈現學習成果（如：口頭報告、手寫報告、製作影片、情境解題等），並避免使用 AI來評量不相關或不適當的內容。

教師可使用AI進行檢視學習評量題目的適切性，提高命題準確性、多樣性及效率。

並可透過示範，讓學生在課堂實作。

1. 組成跨域教師社群，以跨域共好理念討論生成式AI之運用方式。

2. 辦理微學分、微課程、跨域工作坊等系列性生成式AI學習方案。
3. 鼓勵教學單位開設融入生成式AI之課程。

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國立臺中教育大學教務處課務組

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國立臺中教育大學教務處課務組

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國立臺中教育大學教務處課務組

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Artificial intelligence is poised to revolutionise the field of education, offering new methods for teaching and enhancing learning experiences. As educators and institutions explore the integration of AI in education, there is a growing need to understand how this technology can be harnessed to support and amplify pedagogical practices. With its ability to personalise learning, provide immediate feedback, and automate administrative tasks, AI has the potential to address some of the most pressing challenges faced by educators today.

The role of AI in education extends beyond merely a facilitator of information; it also can transform traditional teaching methodologies. By analysing vast amounts of data, AI systems can identify gaps in knowledge and cater to students' individual needs, providing a more tailored and practical learning journey. Incorporating AI into the educational sphere encourages a fluent collaboration between technology and traditional teaching, promising to make education more accessible and inclusive.

However, the integration of AI within teaching and learning also raises essential considerations regarding ethics, privacy, and the future role of educators. Stakeholders must engage in discussions about these challenges to ensure that AI deployment will benefit teachers and learners without compromising human values. As the AI landscape within education evolves, a balanced approach that respects this technology's possibilities and limitations will be critical to its success.

The deployment of artificial intelligence (AI) within educational settings is redefining the teaching and learning paradigms, marrying technology with pedagogical strategies.

Artificial intelligence has significantly progressed from basic algorithms to sophisticated generative AI and large language models. One notable advancement is GPT-4, a model that demonstrates an extraordinary capacity to understand and generate human-like text, making it a revolutionary tool for personalised learning experiences. Similarly, Google's counterpart, BARD, utilises AI to foster interactive learning, providing students and educators with a powerful assistive tool. These technologies have gradually transitioned from supportive roles to integral components in educational systems.

Incorporating AI literacy into education ensures that learners are not just passive recipients but active participants in the AI ecosystem. AI literacy encompasses understanding the functionality of AI technologies, their practical applications, and the ethical considerations surrounding their use. Programmes aim to equip students with the skills to navigate and leverage AI, with a focus on critical thinking and problem-solving. For teachers, this entails embracing AI as a co-educator to augment traditional teaching methodologies.

Incorporating artificial intelligence into educational settings has been transformative, allowing for tailored curriculum development and enhanced assessment capabilities that address the needs of individual learners.

Artificial intelligence offers many applications in developing curricula that align with specific learning outcomes. AI tools are adept at analysing vast amounts of data to inform the creation of syllabi and course materials. Teachers harness these tools to design lesson plans that are comprehensive and adaptive to their students' unique learning paces and styles. This customisation ensures that student learning is supported effectively at every stage.

For instance, AI systems can evaluate current academic research or analyse trends in learning to suggest updates to the curriculum. Additionally, AI can support the generation of assignments that reinforce core concepts, enhance critical thinking, and foster practical application of knowledge.

The use of AI extends into the realm of assessment and feedback, offering a level of precision and personalisation previously unattainable. Through tools like automated grading systems, instructors can provide timely and detailed feedback on quizzes and exams. These systems relieve educators' administrative burden and standardise the grading process to ensure fairness.

Beyond grading, AI in education systems can predict student performance, proactively identifying individuals requiring additional attention or resources. This capability enables educators to intervene effectively, promoting positive learning outcomes. AI-enhanced tools also offer students immediate feedback on their work, allowing them to understand their progress and areas for improvement instantly.

Integrating AI into pedagogy has led to the development of sophisticated tools for content creation and tutoring, reshaping how educational materials are produced and how students engage with learning.

Generative AI tools like DALL-E have revolutionised the creation of digital educational content. They facilitate the quick production of customised images, diagrams, and other visual aids that can enhance teaching materials and help explain complex concepts in a more accessible way. This saves educators time and allows for the tailoring of content to meet diverse learning needs.
Key Features of Generative AI Tools:

Customisation: Ability to generate specific images or content based on textual prompts.

Efficiency: Rapid production of a wide range of educational materials.

Engagement: Creation of visually appealing content that captures students' interest.

AI tutors represent a significant leap in providing personalised learning experiences. Systems like intelligent tutoring can assess a student's current understanding, provide tailored feedback, and adaptively present new challenges to facilitate learning at an optimal pace. These AI tutors, supported by machine learning and chatbots, are increasingly capable of simulating human-like tutoring, making learning more intuitive and responsive to individual needs.

Adaptive Learning Paths: AI can design custom lesson plans that adapt to the pace and style of each student's learning.

Immediate Feedback: Students receive real-time query responses, enabling a more dynamic learning process.

With the integration of AI in education, they are seeing a shift towards more personalised and efficient learning experiences through AI-driven content creation and tutoring systems.

Incorporating Artificial Intelligence (AI) into education necessitates careful consideration of ethical implications to maintain fairness and academic integrity while providing equal access to technological resources.

Bias in AI can skew educational experiences and outcomes. If an AI system is trained on unrepresentative or prejudiced data, it can perpetrate and amplify existing inequities. Mitigating bias requires rigorous validation of algorithms and data sets to ensure fairness in AI-driven educational tools. For instance, Artificial intelligence in education: Addressing ethical challenges in K-12 settings highlights the need for transparency in AI applications to safeguard against such biases and preserve the principle of academic integrity.

Ensuring academic integrity in an era where AI can generate essays or solve complex problems is vital. Institutions should develop clear guidelines on using AI assistance by students, promoting honest practices while leveraging AI's potential to support learning. This includes clear communication on what constitutes permissible aid versus dishonesty.

Equitable access to AI resources is critical in avoiding the deepening of the digital divide. All students, irrespective of their socio-economic background, should have the opportunity to benefit from AI in education. Ethical AI for Teaching and Learning suggests that educators play a crucial role in ensuring that AI tools are used responsibly and that every learner has the necessary support to use these resources effectively.

It is essential to address disparities in access to technology by providing the necessary infrastructure and training for students and educators alike. Strategies include subsidised technology programmes, educational software investments, and resources tailored to varied learning environments and needs.

In education, the synergy between technological advancements and pedagogical knowledge creates new frontiers. This section focuses on how partnerships and emerging technologies are shaping a future where AI augments the teaching and learning experience.

Collaboration between educational institutions and AI experts is crucial in integrating AI into educational practices. Wharton Interactive, an initiative from the Wharton School, exemplifies how partnerships can facilitate the development of AI tools tailored to the science of learning. These alliances typically involve consultation and continuous knowledge exchange, ensuring that AI solutions are designed to address real educational challenges. Through these robust partnerships, educators can harness AI to enhance their teaching methodologies and support student learning outcomes.

The application of emerging technologies in education is transforming the sector's landscape. Technologies such as AI-based analytics engines and adaptive learning systems are reshaping how students engage with content and informing educators on the effectiveness of teaching strategies. Additionally, AI-enhanced education leverages data-driven insights to personalise the learning journey for students, catering to different learning styles and needs. The educational community is better equipped to foster a more inclusive and dynamic learning environment by embracing these technological advancements.

In education, integrating AI into teaching practices is paving the way for enhanced learning experiences, promoting critical thinking, and providing subject-specific support for both the humanities and sciences.

AI-driven educational tools are revolutionising the way educators implement scaffolding techniques. These AI systems are designed to offer learners a customised experience, adjusting the complexity of tasks to match their proficiency levels. For example, AI-powered applications such as ChatGPT can pose progressively challenging questions, ensuring students' understanding is robust before moving to more complex material.

Particular emphasis is also placed on fostering critical thinking skills. AI tools can stimulate deeper analysis by presenting scenarios that require evaluation and synthesis. They can guide students through logical reasoning processes, prompting them to question, infer, and deduce - essential skills across all subjects and disciplines.

AI goes beyond simple fact-recognition in the humanities to encourage interpretation and critique. Learners are exposed to AI-generated content that can enhance their understanding of historical contexts, literary analysis, or philosophical debates. By interacting with AI, students can dissect nuanced arguments or receive immediate feedback on their interpretative claims.

The sciences benefit from AI's capacity to manage and interpret large data sets. Educational AI tools can simulate laboratory experiences or provide instant access to complex calculations, making science education more accessible and interactive. AI applications assist in teaching scientific concepts by visualising data, modelling outcomes, and enabling learners to engage with empirical evidence innovatively.

As artificial intelligence (AI) advances, the impact on education and the nature of work becomes increasingly significant. Integrating AI into educational settings not only transforms learning experiences but also necessitates a rethinking of pedagogical approaches to prepare students and educators for a future where AI is ubiquitous.

In response to the evolving demands of AI-integrated workplaces, there is a pressing need to equip students with robust skills that transcend traditional academic knowledge. This includes fostering critical thinking, problem-solving, and adaptability. To achieve this, educational curricula are being reimagined to align with future workplace needs, where AI tools are common. By integrating AI into learning modules, students gain firsthand experience with the technology, preparing them for proficient use of AI in their future careers.

For instance, collaborative projects that utilise AI-based applications can teach students how to harness the power of AI to solve complex problems. These pragmatic experiences are vital to ensure that their transition into AI-enhanced work environments is seamless.

Teachers themselves must be noticed in the shift towards an AI-enhanced educational landscape. Professional development for educators is crucial to ensure they are equipped with the necessary skills to guide and support students effectively. Teacher training programmes are evolving to include components on AI literacy and integrating AI into pedagogy.

The goal is to familiarise teachers with AI technology and empower them to use these tools to improve learning outcomes. Workshops and training sessions that focus on the practical applications of AI in the classroom help teachers become proficient in leveraging AI for differentiated instruction, personalising learning experiences, and managing administrative tasks more efficiently. This shift underscores the role of AI as a partner in education, augmenting the capabilities of teachers and enhancing their instructional strategies.

In education, the integration of AI has led to the creation of dynamic and adaptive learning materials. These technological enhancements present opportunities and challenges in crafting curricula and syllabi.

Educators increasingly turn to AI-enhanced tools to develop syllabi and educational materials offering personalised learning experiences. These AI systems analyse student performance data to recommend tailor-made resources, optimising the learning curve. Such personalised syllabi can adjust the complexity of subjects based on an individual's mastery, making them highly flexible and student-centred.

Applications: AI applications range from automated content generation to real-time feedback systems, reducing educators' time on administrative tasks and enhancing their capacity to focus on teaching.

Cost: The initial cost of implementing AI tools can be substantial; however, over time, these tools may reduce operational expenses through automation and resource optimisation.

AI-Enhanced Course Materials: AI-powered platforms aid in creating adaptive learning modules that shift in difficulty based on student performance, ensuring that each student can progress at an appropriate pace.

Educators and institutions must consider the financial implications of AI integration, balancing the advantages of customised learning experiences and the affordability of such technologies. When applied judiciously, AI can elevate the quality and accessibility of education, offering a richer, more individualised learning journey.

Artificial intelligence (AI) is steadily reshaping the landscape of education. It offers innovative solutions and strategies that cater to the diverse needs of learners. Best practices in integrating AI within educational systems ensure that teaching methods evolve, fostering a supportive environment for life-long learning.

One should consider AI as a companion in education that enhances, rather than replaces, the human element in teaching. Personalised learning experiences, made possible through AI, empower students by adapting to their learning paces and styles. Moreover, AI's role in data analysis helps educators make informed decisions, further elevating the quality of instruction.

Teachers equipped with AI tools can identify and address learning gaps more effectively, contributing to a more equitable educational experience for all students. The push towards AI-informed pedagogy necessitates ongoing professional development, ensuring educators are well-versed in emerging technologies.

The importance of ethical considerations in the deployment of AI in education must be emphasised. Ethical challenges must be addressed proactively to preserve the integrity and fairness of educational outcomes.

In conclusion, embracing AI in educational frameworks promises a dynamic, inclusive, and innovative future for teaching and learning. Emphasising best practices, educators and policymakers can harness AI's potential to enrich education and support learners throughout their educational journey.

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Artificial intelligence is poised to revolutionise the field of education, offering new methods for teaching and enhancing learning experiences. As educators and institutions explore the integration...

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A third space for dialogues on creative pedagogy: Where hybridity becomes possible

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A third space for dialogues on creative pedagogy: Where hybridity becomes possible

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Use of AI tools in Assessment and Teaching | Centre for Teaching Excellence

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Clearly communicate expectations and guidelines for using generative AI tools to students. This could include pointers such as the types of tasks the generative AI tool is appropriate for, what the limitations of the tool are, and how the AI-generated responses will be used to help student learning.

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International Journal of STEM Education 10, Article number: 29 (2023) Cite this article

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Abstract

The successful irruption of AI-based technology in our daily lives has led to a growing educational, social, and political interest in training citizens in AI. Education systems now need to train students at the K-12 level to live in a society where they must interact with AI. Thus, AI literacy is a pedagogical and cognitive challenge at the K-12 level. This study aimed to understand how AI is being integrated into K-12 education worldwide. We conducted a search process following the systematic literature review method using Scopus. 179 documents were reviewed, and two broad groups of AI literacy approaches were identified, namely learning experience and theoretical perspective. The first group covered experiences in learning technical, conceptual and applied skills in a particular domain of interest. The second group revealed that significant efforts are being made to design models that frame AI literacy proposals. There were hardly any experiences that assessed whether students understood AI concepts after the learning experience. Little attention has been paid to the undesirable consequences of an indiscriminate and insufficiently thought-out application of AI. A competency framework is required to guide the didactic proposals designed by educational institutions and define a curriculum reflecting the sequence and academic continuity, which should be modular, personalized and adjusted to the conditions of the schools. Finally, AI literacy can be leveraged to enhance the learning of disciplinary core subjects by integrating AI into the teaching process of those subjects, provided the curriculum is co-designed with teachers.

Introduction

In recent years, the convergence of huge computing power, massive amounts of data and improved machine learning algorithms have led to remarkable advances in Artificial Intelligence (AI) based technologies, which are set to be the most socially and economically disruptive technologies ever developed (Russell, 2021). The irruption of AI-based technology in our daily lives (e.g., robot vacuum cleaners, real-time location and search systems, virtual assistants, etc.) has generated a growing social and political interest in educating citizens about AI. The scientific community has also begun to engage in this education after detecting a significant gap in the understanding of AI, based on comments and fears expressed by citizens about this technology (West & Allen, 2018). Therefore, integrating AI into curricula is necessary to train citizens who must increasingly live and act in a world with a significant presence of AI.

It is worth noting that AI education addresses not only the learning of the scientific and technological foundations of AI, but also the knowledge and critical reflection on how a trustworthy AI should be developed and the consequences of not doing so. Hence, it is crucial to incorporate AI teaching from the earliest stages of education (Heintz, 2021). However, although some countries are making significant efforts to promote AI teaching in K-12 (Touretzky et al., 2019a), this is being implemented through highly varied AI training experiences, such as data-driven design (Vartiainen et al., 2021), interactive data visualizations (Chittora & Baynes, 2020; von Wangenheim et al., 2021), virtual reality and robotics (Narahara & Kobayashi, 2018), games (Giannakos et al., 2020), or even based on combined workshop series (Lee et al., 2021). To date, there are very few methodological proposals on how to introduce the AI curriculum in K-12 education (Lee et al., 2020).

Since the development of a field requires prior research, we propose in this paper to identify and examine the way in which AI literacy is developing in K-12 around the world, to draw conclusions and guide teaching proposals for AI literacy in K-12. By highlighting and discussing the pros and cons of the different approaches and experiences in the literature, we aim to inspire new initiatives and guide the actors involved, from decisions-makers, for example in education policy, to teachers involved in their conception, design and implementation. We also hope to raise awareness of the importance of learning about AI from an early age, emphasizing the key aspects of this training and, hopefully, fueling the debate that needs to be fostered in our research community.

Integration of AI into the K12 curriculum

As a scientific-technological field, AI is just a few decades old. The name was coined in 1956, and since then different disciplines (such as computer science, mathematics, philosophy, neuroscience, or psychology) have contributed to its development from an interdisciplinary focus. AI is oriented to comprehend, model, and replicate human intelligence and cognitive processes into artificial systems. Currently, it covers a wide range of subfields such as machine learning, perception, natural language processing, knowledge representation and reasoning, computer vision, among many others (Russell & Norvig, 2021).

Starting in the 1970s, AI began to emerge in educational contexts through tools specifically designed to support learning, teaching, and the management of educational institutions. Since many jobs are now AI-related and will continue to increase in the coming years, some researchers believe that AI education should be considered as important as literacy in reading and writing (Kandlhofer et al., 2016). The highly interdisciplinary character is also another factor to consider. AI literacy can be defined as a set of skills that enable a solid understanding of AI through three priority axes: learning about AI, learning about how AI works, and learning for life with AI (Long & Magerko, 2020; Miao et al., 2021). The first axis focuses on understanding AI concepts and techniques to enable the recognition of which artifacts/platforms use AI and which do not. The second axis addresses the understanding of how AI works, to effectively interact with it. The third axis seeks to understand how AI can affect our lives, allowing us to critically evaluate its technology. Thus, AI literacy goes beyond the use of AI applications in education, such as Intelligent Tutoring Systems (ITS) (du Boulay, 2016).

Nowadays, research on AI teaching is still scarce (Chai et al., 2020a, 2020b; Lee et al., 2020). The acquisition of knowledge in AI represents a great pedagogical challenge for both experts and teachers, and a cognitive challenge for students (Micheuz, 2020). Some countries are making significant efforts to promote AI education in K-12 (Touretzky et al., 2019b), by developing relatively comprehensive curriculum guidelines (Yue et al., 2021). Through interviews with practitioners and policy makers from three different continents (America, Asia and Europe), some studies report on continuing works to introduce AI in K-12 education (He et al., 2020). Some other work focuses on examining and comparing AI curricula in several countries (Yue et al., 2021). In addition, there are a growing number of AI training experiences that explore pathways to optimize AI learning for K-12 students. However, most of them are somehow thematically limited as they do not adequately address key areas of AI, such as planning, knowledge representation and automated reasoning (Nisheva-Pavlova, 2021). Additionally, due to the rapid growth of AI, there is a need to understand how educators can best leverage AI techniques for the academic success of their students. Zhai et al. ( 2021) recommend that educators work together with AI experts to bridge the gap between technique and pedagogy.

Using a systematic review method, our research aims to present an overview of current approaches to understand how AI is taught worldwide. Several studies have conducted systematic reviews concerning applications of AI in education. Zhai et al. ( 2021) analyzed how AI was applied to the education domain from 2010 to 2020. Their review covers research on AI-based learning environments, from their construction to their application and integration in the educational environment. Guan et al. ( 2020) reviewed the main themes and trends in AI research in education over the past two decades. The authors found that research on the use of AI techniques to support teaching or learning has stood the test of time and that learner profiling models and learning analytics have proliferated in the last two decades. Ng et al. ( 2022) examined learner types, teaching tools and pedagogical approaches in AI teaching and learning, mainly in university computer science education. Chen et al. ( 2020) covered education enhanced by AI techniques aimed to back up teaching and learning. All these studies have focused on the main role that AI has played in educational applications over the last decades. However, in light of the recent need to consider how AI education should be approached at the K-12 level (Kandlhofer et al., 2016; Long & Magerko, 2020; Miao et al., 2021; Touretzky et al., 2019b), it would be of great value to structure and characterize the different approaches used so far to develop AI literacy in K-12, as well as to identify research gaps to be explored. Recently, Yue et al. ( 2022) analyzed the main components of the pedagogical design in 32 empirical studies in K-12 AI education and Su et al. ( 2022) examined 14 learning experiences carried out in the Asian-Pacific region. These components included target audience, setting, duration, contents, pedagogical approaches to teaching, and assessment methods. Sanusi et al. ( 2022) reviewed research on teaching machine learning in K-12 from four perspectives: curriculum development, technology development, pedagogical development, and teacher training development. The findings of the study revealed that more studies are needed on how to integrate machine learning into subjects other than computer science. Crompton et al. ( 2022) carried out a systematic review on the use of AI as a supporting tool in K-12 teaching, which entails an interesting but narrower scope. Our study extends previous reviews on K-12 AI research by emphasizing how the current approaches are integrating AI literacy in K-12 education worldwide.

Research question

To begin the systematic review, a single research question (RQ) was formulated.

RQ: How are current approaches integrating AI literacy into K-12 education worldwide?

In essence, the RQ aims to investigate the characterization of the different approaches being employed to incorporate AI education in K-12. The following subsections in the methodology describe the search and the data collection process in such a way that an answer to the RQ can be provided in a replicable and objective fashion.

Methods

The research method chosen to conduct this research was the systematic literature review (SLR), following the guidelines posed by Kitchenham ( 2004). Accordingly, the following subsections summarize and document the key steps implemented in this research method.

Search process

We used Scopus to implement the search process. Scopus provides an integrated search facility to find relevant papers in its database based on curated metadata. It includes primary bibliographic sources published by Elsevier, Springer, ACM, and IEEE, among others. It provides a comprehensive coverage of journals and top-ranked conferences within fields of interest. We did not limit our search to specific journals or regular conference proceedings, as there is not yet a clearly established body of literature on the subject. All searches were performed based on title, keywords and abstract, and conducted between 21 October 2021 and 9 March 2023.

TITLE-ABS-KEY ( ( ( ( literacy OR education) AND ( ( artificial AND intelligence))) OR ( "AI literacy" OR "AI education")) AND ( "primary school" OR "secondary school" OR k-12 OR "middle school"

OR "high school") AND NOT ( "computer-based learning") AND NOT ( "intelligent tutoring system")).

Table 1 Summary of search terms and issues associated with their choice

We included peer-reviewed papers published on topics related to literacy and education on AI at school. Then we excluded papers whose usage of AI was limited to 1) supporting computer-based learning only, with no focus on learning about AI; 2) supporting assessment/tutoring based on AI. We also excluded papers that targeted college students and those that were limited to K-12 programming/CS concepts as a prerequisite for learning about AI in the future. Following these inclusion and exclusion criteria, our search in Scopus returned an initial list of 750 documents. After we inspected the title, abstract, keywords and full-text screening, we obtained a final list of 179 documents.

Data collection extraction and synthesis strategy

Data collection extraction was performed, discussed, and coordinated through regular meetings. After inspecting and discussing 10% of the papers over multiple meetings, the authors agreed on the annotations presented in Table 2. This process is important as it allowed us to build a data annotation scheme empirically emerging from the sampled papers. A copy of the papers was also kept for easy review in case of doubts or disagreements.

Table 2 Data extraction annotations definitions

The data resulted in a spreadsheet with the metadata of the papers which passed the inclusion and exclusion criteria, and a document with the list of paper IDs together with the rest of annotations. Some Python scripts were used to further process metadata (e.g., counting participating countries, frequencies, etc.) and produce a more complete bibliographic report with histograms and overview counting. A more qualitative analysis was carried out to answer the research question based on paper reading and annotations.

Results

The results were organized into two subsections. The first subsection is a bibliometric analysis of the reviewed studies, which is based on the metadata provided by Scopus. The second subsection provides a qualitative analysis of the studies, which is based on the extracted data annotations (see Table 2). Both analyses are complementary and together deliver a better understanding of the research articles retrieved.

Bibliometric analysis

Figure 1 shows that the annual scientific production has been modest. It gained traction in 2016 and increased sharply in 2020.

Most of the contributions are conference publications (126 papers), while 52 are journal articles and one is a book chapter (Fig. 2).

Eighty out of 179 papers have at least a citation in Scopus. There are 13 papers that have 10 or more citations, and the most cited papers are Long and Magerko ( 2020) and Touretzky et al. ( 2019b). Figure 3 summarizes the number of contributions by publishers, where Springer, IEEE and ACM stand out, followed by Elsevier. As for journals, there are no single journals concentrating the publication of articles. Nevertheless, there are some journals that are especially relevant and well-known by the community such as the International Journal of Child-Computer Interaction, Computers and Education: Artificial Intelligence, International Journal of Artificial Intelligence in Education, or IEEE Transactions on Education.

As for conferences, Fig. 4 summarizes the main conference events where papers are published. It includes flagship conferences1 such as CHI and AAAI, top-ranked conferences such as HRI or SIGCSE and several noteworthy events (IDC, ICALT, ITiCSE, VL/HCC, to name a few). It is worth mentioning that AAAI is receiving contributions from recent years, which confirms the interest in the field in broadening the discussion to education. There are some additional publications associated with satellite AAAI events, such as workshops in CEUR-WS that deal with the issue under study. Although such contributions may sometimes be short, we decided to include them as they were relevant. For instance, the works published in (Herrero et al., 2020) and (Micheuz, 2020) include the German countrywide proposal for educating about AI, through a 6-module course focusing on explaining how AI works, the social discourse on AI and reducing existing misconceptions. On the other hand, Aguar et al. ( 2016) talk about teaching AI via an optional course which does not contribute to the final grades.

The analysis did not reveal particularly outstanding institutions (see Table 3 for a summary). Among the 299 affiliated institutions, we mostly find universities and research centers along with a few collaboration associations. The most active institutions are the Chinese University of Hong Kong, University of Eastern Finland and MIT, whose authors participated in a total of 19, 11 and 10 contributions, respectively.

Table 3 Most active institutions

Finally, the retrieved papers were co-authored by 643 different authors affiliated to research institutions from 42 countries. Figure 5 shows the histogram of participation by country. Of the 179 papers reviewed, most papers were written by authors affiliated with institutions in the same country. Only 32 papers involved authors from several countries. It is remarkable that in these cases at least one author is from the US, Hong Kong or China.

Literature analysis

By analyzing the data extracted, the papers were classified into two broad thematic categories according to the type of educational approach, namely, learning experience and theoretical perspective. The first category covers AI learning experiences focused on understanding a particular AI concept/technique or using specific tools/platforms to illustrate some AI concepts. The second category involves initiatives for the implementation of AI education for K-12 through the development of guidelines, curriculum design or teacher training, among others. Each main category was further subdivided into other subcategories to structure the field and characterize the different approaches used in developing AI literacy in K-12. Figure 6 shows all the identified categories and subcategories.

Learning experiences focused on understanding AI

This category covers learning experiences aimed at experimenting and becoming familiar with AI concepts and techniques. Based on the priority axes in AI literacy (Long & Magerko, 2020; Miao et al., 2021), we identified experiences aimed at acquiring basic AI knowledge to recognize artifacts using AI, learning how AI works, learning tools for AI and learning to live with AI.

Learning to recognize artifacts using AI

This subcategory refers to experiences that aim to understand AI concepts and techniques enabling the recognition of which artifacts/platforms use AI and which do not. Four studies were found in this subcategory. They are proposals aimed at helping young people to understand and demystify AI through different types of activities. These activities included conducting discussions after watching AI-related movies (Tims et al., 2012), carrying out computer-based simulations of human-like behaviors (Ho et al., 2019), experimenting as active users of social robots (Gonzalez et al., 2017) and programming AI-based conversational agents (Van Brummelen et al., 2021b).

Learning about how AI works

This topic covers proposals designed to understand how AI works to make user interaction with AI easier and more effective. In this type of proposal, the focus is on methodology and learning is achieved through technology (Kim et al., 2023). The objective is to provide a better understanding of a particular aspect of reality in order to carry out a project or solve a problem (Lenoir & Hasni, 2016). The activities are supported by active experiences based on building and creating intelligent devices to achieve the understanding of AI concepts following the idea of Papert's constructionism.

These experiences are mainly focused on teaching AI subfields such as ML or AI algorithms applied to robotics. Understanding the principles of ML, its workflows and its role in everyday practices to solve real-life problems has been the main objective of some studies (Burgsteiner et al., 2016; Evangelista et al., 2019; Lee et al., 2020; Sakulkueakulsuk et al., 2019; Vartiainen et al., 2021). In addition, there are also experiences focused on unplugged activities that simulate AI algorithms. For example, through classic games such as Mystery Hunt, one can learn how to traverse a graph without being able to see beyond the next path to be traversed (blind search) (Kandlhofer et al., 2016). Similarly, the AI4K12 initiative (Touretzky et al., 2019b) collects a large set of activities and resources to simulate AI algorithms.

Learning tools for AI

This type of proposal aims to introduce AI through tools that enable the use of AI techniques. It is therefore an approach focused on learning by using AI-oriented tools. In this vein, different experiences have focused on learning programming tools for applications based on Machine Learning (Reyes et al., 2020; Toivonen et al., 2020; von Wangenheim et al., 2021; Wan et al., 2020), robotics (Chen et al., 2017; Eguchi, 2021; Eguchi & Okada, 2020; Holowka, 2020; Narahara & Kobayashi, 2018; Nurbekova et al., 2018; Verner et al., 2021), programming and the creation of applications (Chittora & Baynes, 2020; Giannakos et al., 2020; Kahn et al., 2018; Kelly et al., 2008; Park et al., 2021). Some of these tools use Scratch-based coding platforms to make AI-based programming attractive to children. In (Kahn et al., 2018), students play around with machine learning to classify self-captured images, using a block-based coding platform.

Learning for life with AI

The ethical and philosophical implications of AI have also been addressed in some experiences ( 2021b; Ali et al., 2021a; Ellis et al., 2005), whereas others focus on training students to participate in present-day society and become critical consumers of AI (Alexandre et al., 2021; Cummings et al., 2021; Díaz et al., 2015; Kaspersen et al., 2022; Lee et al., 2021; Vartiainen et al., 2020).

Proposals for implementation of AI learning at the K-12 level

In China, the Ministry of Education has integrated AI into the compulsory secondary school curriculum (Ottenbreit-Leftwich et al., 2021; Xiao & Song, 2021). Among their schemes we can reference the AI4Future initiative of the Chinese University of Hong Kong (CUHK), which promotes the co-creation process to implement AI education (Chiu et al., 2021). In Singapore, a program for AI learning in schools has also been developed, where K-12 children learn AI interactively. However, the program is hindered by a lack of professionals (teachers) with adequate training (Heintz, 2021). In Germany, there are also several initiatives to pilot AI-related projects and studies (Micheuz, 2020), including the launch of a national initiative to teach a holistic view of AI. This initiative consists of a 6-module course aimed at explaining how AI works, stimulating a social discourse on AI and clarifying the abundant existing misconceptions (Micheuz, 2020). Canada has also designed an AI course for high schools. The course is intended to empower students with knowledge about AI, covering both its philosophical and conceptual underpinnings as well as its practical aspects. The latter are achieved by building AI projects that solve real-life problems (Nisheva-Pavlova, 2021).

The literature also highlights the different approaches that AI literacy should focus on: curriculum design, AI subject design, student perspective, teacher training, resource design and gender diversity. All these approaches are described in depth below.

AI literacy curriculum design

Approaches to curriculum development differ widely, ranging from the product-centered model (technical-scientific perspective) to the process-centered model (learner perspective) (Yue et al., 2021). AI literacy can be launched in primary and secondary education depending on the age and computer literacy of the students. To do this, it is necessary to define the core competencies for AI literacy according to three dimensions: AI concepts, AI applications and AI ethics and security (Long & Magerko, 2020; Wong et al., 2020). Research has focused on the understanding of the concepts, the functional roles of AI, and the development of problem-solving skills (Woo et al., 2020). This has led to proposing a redefinition of the curriculum (Han et al., 2019; Malach & Vicherková, 2020; Zhang et al., 2020) supported by different ideas that K-12 students should know (Chiu et al., 2021; Sabuncuoglu, 2020; Touretzky et al., 2019b). Several countries have already made different curricular proposals (Alexandre et al., 2021; Micheuz, 2020; Nisheva-Pavlova, 2021; Ottenbreit-Leftwich et al., 2021; Touretzky et al., 2019b; Xiao & Song, 2021), where they argue that the curricular design must include different elements such as content, product, process and praxis (Chiu, 2021). It is also convenient for learning in AI to follow the computational thinking model (Shin, 2021), contextualizing the proposed curriculum (Eguchi et al., 2021; Wang et al., 2020) and providing it with the necessary resources for teachers (Eguchi et al., 2021). In this sense, emerging initiatives highlight the need to involve teachers in the process of co-creating a curriculum associated to their context (Barlex et al., 2020; Chiu et al., 2021; Dai et al., 2023; Lin & Brummelen, 2021; Yau et al., 2022).

AI as a subject in K-12 education

Student perspective on AI Literacy

Student-focused studies explore and analyze attitudes and previous knowledge to make didactic proposals adapted to the learner. Some of them measure their intention and interest in learning AI (Bollin et al., 2020; Chai et al., 2021, 2020a, 2020b; Gao & Wang, 2019; Harris et al., 2004; Sing, et al., 2022; Suh & Ahn, 2022), whereas others discuss their views on the integration of technologies in the education system (Sorensen & Koefoed, 2018) and on teaching-learning support tools in AI (Holstein et al., 2019).

Teacher training in AI

Teachers are key players for the integration of AI literacy in K-12, as proven by the numerous studies that examine this issue (An et al., 2022; Bai & Yang, 2019; Chiu & Chai, 2020; Chiu et al., 2021; Chounta et al., 2021; Judd, 2020; Kandlhofer et al., 2019, 2021; Kim et al., 2021; Korenova, 2016; Lin et al., 2022; Lindner & Berges, 2020; Oh, 2020; Summers et al., 1995; Wei et al., 2020; Wu et al., 2020; Xia & Zheng, 2020). This approach places teachers at the center, bearing in mind what they need to know so as to integrate AI into K-12 (Itmazi & Khlaif, 2022; Kim et al., 2021). The literature analyzed reports on the factors that influence the knowledge of novice teachers (Wei, 2021) and focuses on teacher training in AI (Lindner & Berges, 2020; Olari & Romeike, 2021). Thus, AI training proposals can be found aimed at both teachers in training (Xia & Zheng, 2020) and practicing educators. Training schemes focus on their knowledge in technologies to facilitate their professional development (Wei et al., 2020) through the TPACK (Technological, Pedagogical and Content Knowledge) teaching knowledge model (Gutiérrez-Fallas & Henriques, 2020). Studies focusing on teachers' opinions on curriculum development in AI are relevant (Chiu & Chai, 2020), as are their self-efficacy in relation to ICT (Wu et al., 2020), their opinions on the tools that support the teaching-learning process in AI (Holstein et al., 2019) and their teacher training in technologies (Cheung et al, 2018; Jaskie et al., 2021). These elements are central to the design of an AI literacy strategy in K-12. Both the co-design of ML curricula between AI researchers and K-12 teachers, and the assessment of the impact of these educational interventions on K-12 are important issues today. At present, there is a shortage of teachers with training in AI and working with teachers in training (Xia & Zheng, 2020) or with teachers in schools (Chiu et al., 2021) is proposed as an effective solution. One of the most interesting analyses of teacher competency proposes the acquisition of this skill for the teaching of AI in K-12, through the analysis of the curricula and resources of AI using TPACK. This model was formulated by (Mishra & Koehler, 2006) and aims to define the different types of knowledge that teachers need to integrate ICT effectively in the classroom. In this regard, it is suggested that teachers imparting AI to K-12 students require TPACK to build an environment and facilitate project-based classes that solve problems using AI technologies (Kim et al., 2021).

AI literacy support resources

Research using this approach focuses on presenting resources that support AI literacy (Kandlhofer & Steinbauer, 2021), considering that the creation of resources and repositories is a priority in supporting this teaching-learning process (Matarić et al., 2007; Mongan & Regli, 2008). However, these resources largely do not meet an interdisciplinary approach and do not embody a general approach to AI development (Sabuncuoglu, 2020).

Gender diversity in AI literacy

Discussion

The huge impact that AI is having on our lives, at work and in every type of organization and business sector is easily recognizable today. No one doubts that AI is one of the most disruptive technologies in history, if not the most. In recent years, the expectations generated by AI, far from being deflated, have only grown. We are still a long way from general-purpose AI, but the application of AI to solve real problems has already taken hold for a wide range of purposes. It is therefore necessary for young people to know how AI works, as this learning will make it easier for them to use these technologies in their daily lives, both to learn and to interact with others.

Like any other technology, the potential uses and abuses of AI go hand in hand with its disruptive capacity. Many social groups and governments are expressing concern about the possible negative consequences of AI misuse. Although it is crucial to adequately regulate the use of AI, education is as important, if not more important, than regulation. Everything, whether good or bad, stems from the education received. Thus, education systems must prepare students for a society in which they will have to live and interact with AI. AI education will enable young people to discover how these tools work and, consequently, to act responsibly and critically. Therefore, AI literacy has become a relevant and strategic issue (Chiu & Chai, 2020).

This systematic review has focused on analyzing AI teaching-learning proposals in K-12 globally. The results confirm that the teaching of basic AI- related concepts and techniques at the K-12 level is scarce (Kandlhofer et al., 2016). Our work shows that there have been, on the one hand, different AI learning experiences and, on the other hand, proposals for the implementation of AI literacy, made at the political level and by different experts. The learning experiences described show that AI literacy in schools has focused on technical, conceptual, and applied skills in some domains of interest. Proposals for AI implementation, especially those defined by the US and China, reveal that significant efforts are being made to design models that frame AI literacy proposals.

We also found that there are hardly any AI learning experiences that have analyzed learning outcomes, e.g., through assessments of learners' understanding of AI concepts. Obviously, this is a result of the infancy of these AI learning experiences at the K-12 level. However, it is important for learning experiences to be based on clearly defined competencies in a particular AI literacy framework, such as those proposed in the literature (Alexandre et al., 2021; Han et al., 2019; Long & Magerko, 2020; Malach & Vicherková, 2020; Micheuz, 2020; Ottenbreit-Leftwich et al., 2021; Touretzky et al., 2019a; Wong et al., 2020; Xiao & Song, 2021; Zhang et al., 2020). Recently, Van Brummelen et al. ( 2021a) designed a curriculum for a five-day online workshop based on the specific AI competencies proposed by Long and Magerko ( 2020). They used several types of questionnaires to assess the quality of the program through the knowledge acquired by the students in these competencies. Therefore, clearly defined competency-based learning experiences can provide a rigorous assessment of student learning outcomes.

The research shows that clear guidelines are needed on what students are expected to learn about AI in K-12 (Chiu, 2021; Chiu & Chai, 2020; Lee et al., 2020). These studies highlight the need for a competency framework to guide the design of didactic proposals for AI literacy in K-12 in educational institutions. This framework would provide a benchmark for describing the areas of competency that K-12 learners should develop and which specific educational projects can be designed. Furthermore, it would support the definition of a curriculum reflecting sequence and academic continuity (Woo et al., 2020). Such a curriculum should be modular and personalized (Gong et al., 2019) and adjusted to the conditions of the schools (Wang et al., 2020). In the teaching of AI, an exploratory education should be adopted, which integrates science, computer science and integral practice (Wang et al., 2020). It should also address issues related to the ethical dimension, which is fundamental to the literacy of K-12 students as it enables them to understand the basic principles of AI (Henry et al., 2021). This training facilitates the development of students' critical capacity, and this is necessary to understand that technology is not neutral and to benefit from and make appropriate use of it. Ethics, complementary to legal norms, enhances the democratic quality of society by setting legitimate limits in the shaping of technological life. In this sense, different AI literacy proposals in K-12 already support the addressing of ethical, social and security issues linked to AI technologies (Eguchi et al., 2021; Micheuz, 2020; Wong et al., 2020). Moreover, considering designing for social good could foster or help to motivate learning about AI (Chai et al., 2021). Without a doubt, all this will impact on the achievement of a more democratic society. Due to the gender gap in issues related to computer science, it is also necessary to address the gender perspective. In this vein, the research proposes, among other strategies, to focus AI literacy on real-world elements since this approach favors the motivation of girls and greater involvement in learning (Jagannathan & Komives, 2019). However, little attention is paid to the undesirable consequences of an indiscriminate and insufficiently thought-out application of AI, both in higher education and especially in K-12. For example, the increase in socio-economic inequality between countries and within countries, resulting from the increasing automation of employment, is of particular concern. This is leading to growing inequality in wages and preservation of human employment, but it is not usually a subject of interest in education.

Currently, the challenges of this AI literacy require an interdisciplinary and critical approach (Henry et al., 2021). We believe that AI literacy can be leveraged to enhance the learning of disciplinary core subjects by integrating AI into the teaching process of those subjects. AI literacy should rely on transferring AI knowledge and methods to core subjects, allowing education to cross disciplinary boundaries, but staying within the framework of disciplinary core subjects. To achieve this change, educators need to take a closer look at the current capabilities of AI. This would enable them to identify all options to improve the core of educational practice and thus optimize the educational process. For example, understanding and using word clouds is a powerful educational strategy to enhance education in core subjects such as science (e.g., to facilitate object classification), language (e.g. to enable the matching of different topics or authors' works), music (e.g., to support the analysis of song lyrics) or social sciences (e.g., to assist in comparing different discourses). Since AI is highly interdisciplinary in nature, it has a broad projection on multiple fields and problems that require a transversal and applied approach. For example, the basic algorithms of ML could be taught in Mathematics and related disciplines, the design of supervised classifiers could be performed for the study of taxonomies in Biology, natural language processing could be used to make the study of a language more attractive, or the ethical issues surrounding AI could be discussed in Philosophy and Social Sciences subjects.

Conclusion

This study aimed to understand how AI literacy is being integrated into K-12 education. To achieve this, we conducted a search process following the systematic literature review method and using Scopus. Two broad groups of AI literacy approaches were identified, namely learning experiences and theoretical perspective. The study revealed that learning experiences in schools have focused mainly on technical and applied skills limited to a specific domain without rigorously assessing student learning outcomes. In contrast, the US and China are leading the way in AI literacy implementation schemes which are broader in scope and involve a more ambitious approach. However, there is still a need to test these initiatives through comprehensive learning experiences that incorporate an analysis of learning outcomes. This work has allowed us to draw several conclusions that can be considered in the design of AI literacy proposals in K-12. Firstly, AI literacy should be based on an interdisciplinary and competency-based approach and integrated into the school curriculum. There is no need to include a new AI subject in the curriculum, but rather to build on the competencies and content of disciplinary subjects and then integrate AI literacy into those subjects. Given the interdisciplinary nature of AI, AI education can break disciplinary boundaries and adopt a global, practical, and active approach in which project-based and contextualized work plays an important role. Secondly, AI literacy should be leveraged to extend and enhance learning in curricular subjects. As a final point, AI literacy must prioritize the competency of teachers and their active participation in the co-design of didactic proposals, together with pedagogues and AI experts.

Availability of data and materials

Last revision round required update the review. Thus, Additional file 1 contains a.csv file with the listing of papers that are not cited but are part of the reviewed papers. The papers cited in text already appear in the Reference section and, therefore, not in the Additional file.

References

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Acknowledgements

Authors would like to thank the reviewers and editors, whose comments and feedback helped us to improve the original manuscript.

Funding

This work has partially been funded by the Spanish Ministry of Science, Innovation and Universities (PID2021-123152OB-C21), and the Consellería de Educación, Universidade e Formación Profesional (accreditation 2019-2022 ED431C2022/19 and reference competitive group, ED431G2019/04) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Centro Singular de Investigación en Tecnoloxías Intelixentes da Universidade de Santiago de Compostela as a Research Center of the Galician University System. This work also received support from the Educational Knowledge Transfer (EKT), the Erasmus + project (reference number 612414-EPP-1-2019-1-ES-EPPKA2-KA) and the Knowledge Alliances call (Call EAC/A03/2018).

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Authors and Affiliations

1. Pedagogy and Didactics Department, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain

Lorena Casal-Otero, Carmen Fernández-Morante & Beatriz Cebreiro

2. Departamento de Electrónica e Computación, Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain

Alejandro Catala & Maria Taboada

3. Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain

Alejandro Catala & Senén Barro

Contributions

All authors have contributed significantly to the authorship of this work in all stages of conceptualization, discussions, definition of the methodology, carrying out the analysis as well as writing-review & editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Alejandro Catala.

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Ethics approval and consent to participate

This research is carried out in accordance to ethics recommendations. As it focuses on a systematic literature review as a research method, ethics approval by the University ethics committee does not apply.

Competing interests

The authors declare that they have no competing interests. The authors have no other relevant financial or non-financial interests to disclose and no further competing interests to declare that are relevant to the content of this article.

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Casal-Otero, L., Catala, A., Fernández-Morante, C. et al. AI literacy in K-12: a systematic literature review. IJ STEM Ed 10, 29 (2023). https://doi.org/10.1186/s40594-023-00418-7
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AI literacy in K-12: a systematic literature review - International Journal of STEM Education

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The successful irruption of AI-based technology in our daily lives has led to a growing educational, social, and political interest in training citizens in AI. Education systems now need to train students at the K-12 level to live in a society where they must interact with AI.

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www.engagemedia.org

Previously, Creative Commons hosted a CC Search Portal on its website (CC Search). This has been updated to Openverse, hosted on the WordPress website. Similar to CC Search, users are still able to search by use case, licence type and several other filters. Openverse also has built in language support, so users now have options for what language to use the search engine in. Openverse has over 600 million CC Licensed items for users to browse and reuse.

This guide is designed to guide you through the steps involved in searching for Creative Commons materials using Openverse. It forms one part of an information pack on CC for the education sector. It is advised that you first read information guide ' What is Creative Commons? ' for a simple and helpful introduction to CC.

The Creative Commons Openverse is not the only way to search for CC material. You can also use Google or go directly to sites that have the type of CC material you're looking for.

For information on how to use google see ' How to find Creative Commons Material using Google '.

For a list of sites that host CC material see: this wiki on Content Directories and Smartcopying's list of OER.

Finding CC materials using the Creative Commons Openverse

Below is a step-by-step guide to using the search tools in the CC Openverse to find content released under Creative Commons licences. The CC Openverse allows users to return very specific results from a number of sources. By reading the metadata embedded in the digital files of content licensed under Creative Commons, the search portal can find content that is available for certain kinds of uses, including works that are available for commercial reuse and works that are available for remixing. By limiting the search to specific sites that only include one type of content (eg videos, photos or music), the CC Openverse also lets you search for specific types of material. This can give it an advantage over the CC Openverse tools provided by Google.

It is important to consider how you want to use any CC material before you begin your search. This is because different CC licences let you do different things. Knowing what you want to do with the material will help you choose the licence that is suitable for your use.

Step 1. Start a CC Openverse Search

Step 2. Filter by the type of material you want to search for

To filter material based on the type of use you want to make, first type in your search terms in the white box. In the drop-down menu, select 'all content', 'images' or 'audio'.

Step 3: Running the search

Now that you've chosen the type of material you want to search, you can enter keywords for your refined search in the search query field and hit 'Search'. Your results will appear on a results page. Down the right-hand side of the results page, you will see options to filter your results.

This gives you two 'use' options: 'I want something that I can use for commercial purposes' and 'I want something that I can modify, adapt'. You should adjust these selections to reflect the option(s) appropriate for your use. You can select one, both or none of the boxes.

The table below shows how each option will filter the material by licence:

Step 3. Running the search

Now that you've chosen the type of licence, you can enter keywords for your refined search in the search query field and hit 'Search'. Your results will appear on a results page.

Step 4. Filtering your results even further

On the results page from your search you are able to further filter your results. On the right-hand side of the results page there are various drop-down menus where you can filter your results for:

Example

You are running a class project on river systems. The students are required to create a video on a river system of their choosing. In order to assist the students with this task, you are preparing a course pack that includes sample images for the students to use.

You want to conduct a search for CC images to include in the course pack. To do this you need to:

1. Go to the Creative Commons Openverse.

2. As the project is designed for classroom use, there is no need to exclude non-commercial content. However, it is likely that the students will want to adapt the images so you need to limit your search for works that can be adapted or modified.

3. Enter 'rivers' into the into the Creative Commons Openverse query field.

Your search returns a number of photos that are exactly the kind of images you were looking for (see screenshot above as an example). You insert them into your course pack, along with the appropriate attribution details. For information on how to attribute CC material, see information guide ' How to Attribute Creative Commons Material '.

Google is one of the most commonly used search engines in the world, but did you know you can use Google to find Creative Commons (CC) licensed content? Using Google Advanced Search, you are able to search for material by keyword and refine your results to show only material available that has certain 'usage rights'. This guide was created to help teachers, students and curriculum units find CC licensed material using Google's Advanced Search. It forms one part of an information pack on CC for the education sector. It is advised that you first read information guide ' What is Creative Commons? ' for a simple and helpful introduction to CC.

Google is not the only way to search for CC licensed videos. Creative Commons has its own search engine, hosted on WordPress, called Openverse http://www.wordpress.org/openverse/.

Or you can go directly to sites that host the type of CC material you are looking for.

For information on how to use the Openverse search portal, see How to find Creative Commons materials using the Openverse Search Portal.

For a list of sites that host CC material see this wiki on Content Directories and Smartcopying's list of OER.

Finding CC materials using Google Advanced Search

Below is a step-by-step guide to using the Google Advanced Search to find content released under certain 'usage rights'. Google Advanced Search is best used to search for CC material across a number of different websites or to search within websites that do not provide their own search system. To search for CC material in Flickr or on Blip.tv, it is recommended that you use the CC Search Portal.

It is important to consider how you want to use any CC material before you begin your search. This is because different CC licences let you do different things. Knowing what you want to do with the material will help you choose the licence that is suitable for your use.

Step 1. Start a Google Advanced Search

There are many ways to get to Google's Advanced Search options. You can access it directly by typing www.google.com/advanced_search into the address bar on your web browser. Alternatively, most people will access the search options by going to the Google homepage ( www.google.com) and clicking 'Settings' on the bottom right hand side of the webpage, and select 'Advanced search' within the drop-down menu (see below). This will bring you to the Google Advanced Search page (see step 2).

Step 2. Filter results for what you want to do

By default Google search results are not filtered by licence. To filter your search results for CC licensed content, scroll down in advanced search and set "usage rights" parameters to be "Free to use, share, or modify". If you want content for commercial use be sure to select the appropriate option.

Select the option from this menu that is appropriate to your use.

All CC licences grant the user the right to use and distribute the licensed content. Therefore, if you select the 'free to use or share' option, your search will return content available under any of the CC licences.

If you select 'free to use, share or modify, even commercially', you will only return results under the two CC licences that allow both adaptation and commercial use (ie Attribution and Attribution-Share Alike).

The table below shows how the drop-down menu filters the material by licence.

* Please note that although this option will theoretically return results under a Creative Commons licence, these will be amongst potentially millions of returned results that are not CC licensed.

Step 3. Search for keywords

Now enter your keyword in the field at the top of the page and click 'Advanced Search' to begin your search. You can also combine Google's 'usage rights' search tool with other fields on the Google Advanced search page to refine your search even further. For example, you can search for CC within a particular site or domain, or in a particular language, or in a particular file format.
Example
You are running a class project on river systems. The students' task is to learn about river systems from around the world and to create a short video about a river system of their choosing. They can use Creative Commons licensed materials and original content made by them. The best video will be entered in a competition run by Brisbane's River festival.

Your students want to find a CC licensed song to use in the soundtrack of their video. They decide to look for an appropriate song on Myspace using Google Advanced Search.

Because they want to use the music in a video, they need to look for material that they can 'change and adapt'. As there is a prize for the competition winners, they aren't sure whether their use is 'non-commercial'. To be on the safe side, they decide to look for a song that they are allowed to use commercially.

To search for Creative Commons music that can be remixed and used for commercial purposes, the students need to:

1. Go directly to the Google Advanced Search or access it through the Google homepage.

2. Expand the 'Date, usage rights, numeric range, and more' section at the bottom of the page by clicking on it.

3. Choose 'free to use, share and modify, even commercially' from the 'Usage Rights' drop down menu.

4. Type 'myspace' in the 'Search within a site or domain:' field, to limit their search to this site. Type 'river' in the 'all these words' field at the top of the page and start the search.

After trying several different keywords, they find a song that they like under a CC Attribution licence. They add it to the film and, in accordance with the licence, include the author, title, source and the CC licence the song is under in the credits of the film. For information on how to attribute CC material, see information guide 'How to Attribute Creative Commons Material'.

YouTube is one of the most commonly used video search engines in the world. Did you know you can use YouTube to find Creative Commons (CC) licensed videos? Using the YouTube search filters, you are able to search for material by keyword and refine your search to show only material available under a CC license. This guide was created to help teachers, students and curriculum units find CC licensed material on YouTube. It forms one part of an information pack on CC for the education sector. It is advised that you first read information guide ' What is Creative Commons? ' for a simple and helpful introduction to CC.

For a list of sites that host CC material see: this wiki on Content Directories and Smartcopying's list of OER.

Finding CC licensed videos on YouTube

Below is a step-by-step guide to using the YouTube filter to find videos released under CC licenses.

It is important to consider how you want to use any CC material before you begin your search. This is because different CC licences let you do different things. Knowing what you want to do with the material will help you choose the licence that is suitable for your use.

Step 1. Do a search and find the filters option

After you do a search on YouTube, click on the filters option.

Step 2. Filter results

Under 'Features' select Creative Commons.

Now all the videos in the search will be licensed under Creative Commons. Note this will return all Creative Commons licensed videos, so you will need to be sure to check out the particular licence and abide by its conditions. For more information on Creative Commons licences and their conditions, see ' What is Creative Commons? '.

All Creative Commons licences require that users of the work attribute the creator. This is also a requirement under Australian copyright law. This means you always have to acknowledge the creator of the CC work you are using, as well as provide any relevant copyright information.

For many users of CC material, attribution is one of the hardest parts of the process. This information guide is designed to help you ensure you are attributing the creator of a CC licensed work in the best possible way.

What to include when attributing a work

'Common-sense' principles apply to providing attribution across all CC licences. When attributing a work under a CC licence you should:

credit the creator

provide the title of the work

provide the URL where the work is hosted

indicate the type of licence it is available under and provide a link to the licence (so others can find out the licence terms)

keep intact any copyright notice associated with the work.

Another way to remember this when attributing is through TASL:

It is important to always check whether the creator has specified a particular attribution.

This may sound like a lot of information, but there is flexibility in the way you present it. With a bit of clever formatting and linking, it is easy to include everything, particularly in the digital environment.

Of course, you do not have to include any information you cannot locate. However, you should make a good effort to find the relevant information for the material you are planning on using. Think about what you would want if it was your material that others were using.

Identifying the creator

Sometimes it can be hard to identify the person who created the material you are using. Use common sense when determining who to attribute.

If you're on a blog or news website with a number of authors, attribute the person associated with the content you are reusing. Try to find a by-line (eg by Joan Citizen) or the name of the person it was submitted by. If there is more than one author of the content, you should attribute them all. It is a good practice in cases such as this to attribute the publication as well.

Other times, it may be difficult to find the name of the creator. This is often the case for websites like Flickr or YouTube, where the author may only be identified by their username. It is always a good idea to see if you can find their legal name, for example in the 'about' or 'biography' section of a website, on a profile page, or in the copyright notice (eg © Joan Citizen 2009). While looking to find the creator's legal name, you should also check whether the creator has asked to be attributed in a particular way. If you can't find the legal name, use the pseudonym or username if there is one.
Example
You want to use this photograph that you found by searching Flickr for 'aquariums'. The photo is under a Creative Commons Attribution 2.0 licence and is posted by user 'Qole Pejorian'.

You aren't sure whether Qole Pejorian is the user's real name, so you click on the profile name (ie 'Qole Pejorian') in the right-hand column which takes you to the user's photostream. From here, you access Qole Pejorian's user profile by clicking on 'Profile' at the top of the photostream page.

On Qole Perjorian's profile page there is a statement which tells you that the photographer's real name is Alan Bruce. It also includes information on how he wishes to be attributed.

I have made all my photos "Attribution Only". This licence says that you need to attribute my photos "in the manner specified by the author ". So this is the manner I wish you to attribute the photo to me: Use my real name (Alan Bruce), link back to my photo page, and please leave a comment on the photo page for any photos that you use for your own purposes.

Following these instructions, you use the following attribution for the photograph, and leave a message on the photo page telling him about your use.

Vivian and the Giant Fish by Alan Bruce, available under a Creative Commons
Attribution Licence 2.0 at www.flickr.com/photos/qole/2284384975

Attributing others

The creator of the work may also require other parties to be included in the attribution, such as co-creators, sponsors or publishers. Where other parties are identified you should also include them in the attribution. If you are unable to provide details of all the parties, be sure to provide details of where you found the work (eg the website, magazine, journal).

Always include the CC licence

Even though it can sometimes be difficult to provide all the information, you must always include the details of the CC licence that the work is available under. This is so other people know that you have permission to use the work, that they can use it as well and under what conditions. As you can see from the example above, the CC licences can be identified in several ways. You can list the licence name in full, use the abbreviated form of the licence or use either the normal or compact licence buttons. This table shows the full licence name, abbreviated form and both licence buttons for each of the six standard CC licences.

Link to the site

As you can see from the above examples, it is also important that, where possible, you provide a link to the site where you obtained the original work. This gives other people the ability to easily access the original work themselves. Depending on the medium in which you are reusing the work, this can be done by either using a hyperlink or the URL as text. If the original work does not have an associated URL, you do not have to link back to the original work.

Remixing the original work

If you change the original work in any way, such as cropping the work, changing the colours or replacing words, you will be creating a derivative work of the original. You should always attribute the original work in any derivative work and identify that changes have been made to it. Often the simplest way to do this is to use the phrase "This work is a derivative of..." and attribute the original work as you would normally. If your work incorporates a number of derivative works, you might say, "This work includes material from the following sources..." and list each original work. It is a good idea to state the order you are listing them in eg "This work includes material from the following sources (listed sequentially)..."
Example
All the content on poet and hip-hop artist CharlieHipHop's website (www.charliehiphop.com) is available under a CC licence. Some of your students decide to use one of Charlie's poems, 'Let's Get Moving', as a rap in a video they are making. They change the words to suit the video topic.

In the right hand column of his site CharlieHipHop displays the licence button and standard notice for the CC Attribution-Noncommercial-ShareAlike licence.

This work is licensed under a

Creative Commons Attribution-Noncommerical- ShareAlike 3.0 Unported Licence.

Attribution Guidelines

Below this he has provided a link to his own guidelines which outline in detail how he would like his songs to be attributed.

Attribution Guidelines

Per licence, you may use work posted under domain charliehiphop.com in whatever non-commercial way you see fit so long as you extend the same rights to others on your finished product. Your use of our work in no way implies endorsement of your work by any contributor to charliehiphop.com. Please acknowledge our contribution to your final work by linking back http://charliehiphop.com.

To comply with the CC licence and these guidelines, your students include this attribution for CharlieHipHop in the credits of their film.

Attributing in different mediums

Where possible, the same information should be included regardless of how you are reusing the work. However, sometimes this is impractical or impossible. The CC licences let you change the exact placement, language or level of detail from medium to medium, as long as your attribution is still 'reasonable to the medium'. For example, when you are using CC material in a book, it is easy to provide a long, written attribution with all the information next to the work, including the name and URL of the licence spelled out in full (this is always a good idea when you are using CC material offline, or in a document that you think people are likely to print out). However, when you use a CC song in a podcast, it is trickier to provide this level of information. Below are suggestions for how you might attribute a CC work in different mediums. Remember, however, to always check whether the creator has specified a particular attribution.

Books, magazines, journals
· Remembering to spell out the licence type and URL in full, include the relevant attribution information next to the CC work or as a footer along the bottom of the page on which appears.

· Alternatively, you can list the CC works in the back of the publication. If you take this option, it is best to indicate the page number of the work or order in which they appear in the publication.

Photos and images
· Provide the relevant attribution next to the photograph, or close by (eg on the edge or bottom of the page) if that is too obtrusive.

Slideshows
· Include the relevant attribution information next to the CC work or as a footer along the bottom of the work on each slide on which the work appears.

· Alternatively, you can include a 'credits' slide at the end of the show, that lists all the materials used and their attribution details. Again, you should indicate the slide or order so people can find the attribution for a specific work.

Film
· Include the relevant attribution information with the work when it appears on screen during the film.

· If this is not possible, attribute the work in the credits, just as you would see in a normal film.

Podcasts
· Mention the name of the artist and that it is under a CC licence during the podcast, like a radio announcement, and provide full attribution on your website, next to where the podcast is available.

Where you should place the attribution

For text resources (eg books, worksheets, PowerPoint slides etc), include the attribution details next to CC work or as the footer along the bottom of the page on which the CC work appears. For video works, include the attribution information near the work as it appears on screen during the video. For sound recordings (eg podcasts), mention the name of the artist during the recording (like a radio announcement) and provide full attribution details in text near the podcast where it is being stored (eg blog, school intranet, learning management system etc).

Keep track of everything you use

Finally, in order to attribute properly, it is important that you keep track of all the materials that you use the as you use them. Finding materials later can be very difficult and time consuming. Use table below to keep track of all the CC material you use as you use it.

Creative Commons licences are designed to let others know how they may use a work without infringing copyright. Therefore, if you are distributing information under a Creative Commons Licence, and it includes content that isn't covered by the Creative Commons Licence, this material needs to be clearly identified.

Third Party Content

One category of content that you are unable to license under a Creative Commons licence is third party content. This is content that is created by someone else, a third party, and as such you do not have the rights to license the content. You may not have the rights for any number of reasons, eg, the content is protected by trademark, owned by someone else, or licensed under another licence (even if it is the same Creative Commons licence that you applied). For all third party content, you must prominently mark or indicate in a notice that this content is excluded from the Creative Commons licence.

How to Label Third Party Content

There is no single correct way to label third party content, and different situations may require more or less complicated notices and marking. There are two often used mechanisms - you can do either or both of these, as appropriate for the medium:

1. Giving a notice next to third party content

This involves marking or notating all third party content. To do this you should indicate directly underneath the content.

Example: direct permission from the copyright owner to use content

'Smith, J 2014, How to label third party content © National Copyright Unit, NSW, all rights reserved, used with permission.'

Example: material licensed under Creative Commons

'Smith, J 2014, How to label third party content, © NSW Department of Education & Communities, used under the Creative Commons Attribution 4.0 International Licence. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.'

Example: Material copied under a statutory licence

'Copied under s 113P, Edgar, S 2002, Mathematics Unplugged © Bristol Press.'

2. Giving a general notice listing all third party content

This involves giving a general notice that identifies all third party content. This notice would usually be included in your terms of use or copyright statement for a website or in the verso page or bibliography for a work.

This notice should specifically identify all third party content. This can be done by listing all the third party content specifically or, where possible, listing material based on content type.

Example: listing all the third party content

'All material on this website, except as identified below, is licensed under the Creative Commons Attribution 4.0 International Licence. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.'

Material that is not licensed under a Creative Commons licence is:

Commonwealth Coat of Arms

Material protected by a trade mark

Logo

Photographs on pages 4, 5 and 6

Poem on page 2

[Etc]

All content not licensed under a Creative Commons licence is all rights reserved, and you must request permission from the copyright owner to use this material.'

Example: identifying third party content by content type

3. Giving a general notice and a notice next to third party content

This involves giving a general notice that indicates any third party content will be identified, and then identifying the third party content within the website/work. This general notice would usually be included in your terms of use or copyright statement for a website or in the verso page or bibliography for a work.

Example: general notice for a website

Example: general notice for a document

Then next to all third party content you can include a notice regarding the relevant copyright owner and the material's terms of use and, where possible, the licence or permission you've received from the copyright owner.

Example: direct permission from the copyright owner to use content

'Smith, J 2014, How to label third party content © National Copyright Unit, NSW, all rights reserved, used with permission.'

Example: material licensed under Creative Commons

Example: Material copied under a statutory licence

'Copied under s 113P, Edgar, S 2002, Mathematics Unplugged © Bristol Press.'

With whatever method you choose, the most important thing is that you clearly and effectively identify third party material.

Information about Creative Commons

Search tools for finding Creative Commons and open access resources

These sites have material which is searchable by how they can be used, including whether they are under a Creative Commons licence:

Web resources where all material is CC licensed or open access

Text

Images

Sound

Video

Education

Collections

Other

Web resources where some material is CC licensed or open access Images

Video

Other

Remember that all CC material you use must be attributed. For information on how to attribute CC material, see How to Attribute Creative Commons Material.

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Creative Commons Information Pack for Teachers and Students - Smartcopying

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Updated 9 January 2022 NCU has recently revised its CC Information Pack for Teachers and Students to incorporate the new CC 4.0 International Licence. The pack explains what CC is, how to find CC material and the best way to attribute CC material. Please click on the following headings to navigate to each section of [...]

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Three ways to create. Infinite possibilities.

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Modify Region

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Give your characters a voice. Type what you want to hear, and Pika will put your words in their mouth.

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The idea-to-video platform that sets your creativity in motion.

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Creative Commons is a nonprofit organization that aims to make licensing your work and accessing public domain resources simple for individuals, educational institutions, and other organizations across the globe.

By providing clearly labeled and easy-to-understand Creative Commons licenses, as well as public domain tools, they have created a user-friendly and standardized way for all of us to grant permissions to and access each others' copyrighted works.
How might Creative Commons open doors to innovation in your teaching, research, or creative endeavors?
Creative Commons Licenses

Creative Commons (CC) licenses are legal tools that creators and other rights holders can use to grant certain usage rights to the public, while reserving other rights. CC licenses are written as an agreement between the author and the general public, making usability and shareability the default - rather than restricting use. Like traditional licenses, they are designed to offer authors and creators legal standing from which to protect their intellectual property and act against those who use it improperly.

Put more plainly, CC licenses allow the end user to know what they can do with a material while the creator retains copyright to the resource. In academic settings, they are most commonly seen applied to open access journal articles, scholarly monographs, or open educational resources, including teaching materials like textbooks, assignments, and simulations.

Creative Commons works are well-suited for teaching, research, and creative works. For example, Creative Commons is a great resource for adding stock or decorative images to your courses or scholarly presentations. Many open access articles and open educational resources are also licensed using Creative Commons.

How the Licenses Work

Each CC license has three layers... Imagine a layer cake where the first layer is the legal code that does all the heavy lifting of extending the rights to the end user. The second layer makes the licenses easily readable by a human-Creative Commons does this with the license shorthand and simple graphic images we'll explore below. The final layer of the cake is machine readable, or written in a way that computers and software understand.

Legal Code: Each license begins as a traditional legal tool, using the jargon-y kind of language and text formats that lawyers know and love.

Human Readable: Each license is also available in a format that an average person can read and easily understand its key terms and conditions.

Machine Readable: Each license has a summary of the key freedoms and obligations written in a format that software systems, search engines, and other technology can understand.

Understanding License Terms

All works held under Creative Commons licenses are indicated with this icon:

. A CC-licensed work can then have four different license terms- attribution (BY), no derivative works (ND), share alike (SA), and non-commercial (NC) -attached to the license to allow certain permissions to the user. Essentially, these four license terms define the rights the user has to the work. Each of these rights is also indicated with a specific icon so that the licenses are clearly labeled for users.

Attribution, "BY" or the

icon, which indicates that a user can copy, adapt, remix, display, perform, and distribute your work so long as they credit your name as requested by you.

No Derivative Works, "ND" or the

icon, which indicates that the work is free to copy, distribute, display, or perform as it is, verbatim with no modifications or adaptations.

Share Alike, "SA" or the

icon, which means that folks can only disseminate the work under the same license chosen by the original author for the work.

Non-Commercial, "NC" or the

icon, which indicates that others may copy, distribute, display, perform, or remix the work but only for non-commercial purposes.

We'll dive into how these rights can be combined to make the six primary Creative Commons licenses in the Building Your CC License section below.

Other Creative Commons Designations

In addition to the six most used CC licenses, there are two additional designations available. The first is the Public Domain Mark, indicated with the

icon, which indicates a work is free of known copyright instructions, and therefore is in the public domain. The second additional designation is the CC0 (i.e., "CC zero") Public Domain Designation, indicated with the

icon. This indicates that the creator of the work has relinquished their copyright to the work and released it into the public domain with "no rights reserved."

Building Your CC License

One way to think about how different CC licenses work together is to imagine building blocks. When you're ready to license a work, review the different license terms and think about the rights you want others to have. Then you can stack the licenses onto each other in different combinations to meet your needs.

Let's break down how to build a license...

Use this infographic to understand the various CC licenses and their terms. ​​​​​This work is a CC0 Public Domain Dedication. Access a transcript of the graphic.

The base layer of your license building blocks is always going to CC BY. As noted above, a CC BY license with no other building blocks means that a user can copy, adapt, remix, display, perform, and share your work as long as they give you credit. CC BY is the most open of the licenses (aside from public domain designations like CC0), meaning it allows you to give the most flexibility to others using your work.

As you decide on the additional permissions (or restrictions) you want your end user to have, you can add more building blocks from the other license terms: ND (No Derivative Works), SA (Share Alike), and NC (Non-Commercial).

The Creative Commons licenses you can build, from most to least open, are listed below.

Can you use the Creative Commons infographic to determine the rights given to the user for each license?

For example... Say you want others to be able to remix, adapt, copy, display, perform and share your work-but only for non-commercial purposes and only with the same CC license you used for the original. Which license would you choose?

Check your understanding of the six primary Creative Commons licenses at the Creative Commons website.

License Compatibility

Because CC licenses can stack on top of each other like this, it is important to think about whether they are compatible with each other when you're authoring or adapting an openly licensed resource.

The chart below indicates CC licenses' compatibility. Note that licenses with a CC BY ND designation cannot be remixed with any other material, as the "no derivatives" license forbids changes being made to the resource.

Choosing the Right License for You

If you'd like help selecting a license for a work you've authored and have the rights to openly license, it is recommended to use the CC Chooser from Creative Commons. It walks you through a decision tree with a series of questions that help you identify the right license for your work. It also provides a form at the end to create an attribution for the work, as well as Rich Text, HTML, and XMP license formatting for the web.

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Teaching English as a Foreign Language (TEFL) has always been a dynamic field, constantly evolving with technological advancements. One of the most exciting developments in recent years is the integration of Artificial Intelligence (AI) into the TEFL classroom. Here's how AI can be a game-changer in English language teaching and learning, along with the challenges it presents and the tools available to cope with these challenges.

Despite its potential benefits, AI also presents several challenges:

To address these challenges, several tools and strategies are available:

Plagiarism Detection Tools: Teachers can use plagiarism detection tools to ensure that students are not misusing AI to complete their assignments. There are several tools available that can help detect AI-generated text, such GPTZero . It's important to note that while these tools can be very helpful, they may not be 100% accurate, and teachers should use their judgment in conjunction with these tools. Also, it's crucial to educate students about the ethical implications of using AI to complete their assignments.

AI has the potential to revolutionize TEFL classrooms, offering numerous opportunities while also presenting certain challenges. By understanding these aspects and using the available tools effectively, educators can harness the power of AI to enhance their teaching and enrich their students' learning experience.

In fact, this article itself serves as an example of the capabilities of AI. It was written in collaboration with an AI chatbot, demonstrating how AI can assist in generating content and providing valuable insights. This collaboration showcases the potential of AI not only in the classroom but also in professional writing and content creation. The future of TEFL and AI is indeed promising! 😊

Teaching English as a Foreign Language (TEFL) has always been a dynamic field, constantly evolving with technological advancements. One of the most exciting developments in recent years is the integration of Artificial Intelligence (AI) into the TEFL classroom. Here's how AI can be a game-changer in English language teaching and learning, along with the challenges it presents and the tools available to cope with these challenges.

Despite its potential benefits, AI also presents several challenges:

To address these challenges, several tools and strategies are available:

Plagiarism Detection Tools: Teachers can use plagiarism detection tools to ensure that students are not misusing AI to complete their assignments. There are several tools available that can help detect AI-generated text, such GPTZero . It's important to note that while these tools can be very helpful, they may not be 100% accurate, and teachers should use their judgment in conjunction with these tools. Also, it's crucial to educate students about the ethical implications of using AI to complete their assignments.

AI has the potential to revolutionize TEFL classrooms, offering numerous opportunities while also presenting certain challenges. By understanding these aspects and using the available tools effectively, educators can harness the power of AI to enhance their teaching and enrich their students' learning experience.

In fact, this article itself serves as an example of the capabilities of AI. It was written in collaboration with an AI chatbot, demonstrating how AI can assist in generating content and providing valuable insights. This collaboration showcases the potential of AI not only in the classroom but also in professional writing and content creation. The future of TEFL and AI is indeed promising! 😊

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How AI Can Revolutionize the TEFL Classroom: Opportunities, Challenges, and Tools

# data[].attributes.attachment_link.description
Teaching English as a Foreign Language (TEFL) has always been a dynamic field, constantly evolving with technological advancements. One of the most exciting developments in recent years is the integration of Artificial Intelligence (AI) into the TEFL classroom.

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Abstract

Generative artificial intelligence (GenAI) can be used to author academic texts at a similar level to what humans are capable of, causing concern about its misuse in education. Addressing the role of GenAI in teaching and learning has become an urgent task. This study reports the results of a survey comparing educators' (n = 68) and university students' (n = 158) perceptions on the appropriate use of GenAI in the writing process. The survey included representations of user prompts and output from ChatGPT, a GenAI chatbot, for each of six tasks of the writing process (brainstorming, outlining, writing, revising, feedback, and evaluating). Survey respondents were asked to differentiate between various uses of GenAI for these tasks, which were divided between student and teacher use. Results indicate minor disagreement between students and teachers on acceptable use of GenAI tools in the writing process, as well as classroom and institutional-level lack of preparedness for GenAI. These results imply the need for explicit guidelines and teacher professional development on the use of GenAI in educational contexts. This study can contribute to evidence-based guidelines on the integration of GenAI in teaching and learning.

Introduction

Public interest in artificial intelligence (AI) has grown substantially as a result of recent public access to large language models (LLMs; e.g., OpenAI's GPT-3 and 4, Google's PaLM 1 and 2), and chatbots (e.g., OpenAI's ChatGPT, Google's Bard, Microsoft's Bing) that allow users to interface with LLMs. These Generative AI (GenAI) tools afford individuals with the ability to instantly generate writing on any topic by inputting a simple prompt. The public discourse surrounding GenAI has been mostly positive, but in the education sector there is serious concern about academic integrity and plagiarism (Dehouche, 2021; Lampropoulos et al., 2023; Sullivan et al., 2023; Yeo, 2023). Some schools have responded by banning the technology outright (Yang, 2023), a move likened by some to the banning of the pocket calculator when it was perceived as a threat to math education (Urlaub & Dessein, 2022). What is clear is that this new technology possesses disruptive potential and that institutions which have relied heavily on student writing for education and assessment will need to respond accordingly.

Although a few schools have banned ChatGPT and similar tools, many have not, displaying confidence that their institution's academic integrity policy is robust enough to accommodate the new technology. However, current definitions of plagiarism have been described as medieval (Dehouche, 2021; Sadeghi, 2019), typically including language such as kidnapping, stealing or misappropriating the work of others (Sutherland-Smith, 2005), which now leads us to question whether a chatbot counts as one of these others. Generative AI is trained on a selection of diverse natural language data from across the Internet which allows it to string together unique combinations of words and phrases, similar to how humans learn to produce an unlimited amount of novel spoken or written text from the limited language they absorb from their environment, a tenet of generative grammar (Chomsky, 1991). The result is that there is no identifiable other whose work is being stolen by a chatbot. To complicate matters, the language of OpenAI's Terms of Use state that it assigns users "all its right, title and interest in and to Output" from ChatGPT, including for purposes of publication (OpenAI, 2023). Any practiced educator would likely agree that submitting an essay written by ChatGPT without disclosure violates academic integrity, but students may not readily see a problem with it.

Although GenAI has multiple applications, its use as an authoring tool in programs like ChatGPT allow for easy misuse. Students who have purposefully violated academic integrity in the past through the use of contract cheating or paper mills will likely not hesitate to use ChatGPT or other GenAI tools to do so now, but other students will need guidance on how to avoid inadvertently cheating. Student perceptions of academic dishonesty have historically been unclear or incomprehensive, and rarely align with teacher expectations (Tatum, 2022), GenAI will only serve to complicate this (Farrokhnia et al., 2023).

Some advocate working towards a coexistence with AI in education by establishing common goals and guided exploration of the limitations of the technology (Godwin-Jones, 2022; Tseng & Warschauer, 2023). Yeo ( 2023) has specifically recommended the exploration of student perceptions about the ethics of using GenAI tools, and Pack and Maloney ( 2023a) suggested teacher and researcher use should also be investigated.

To date no consensus has arisen regarding what constitutes appropriate use of GenAI in higher education. Therefore, with the goal of identifying some common expectations, the purpose of this study is to explore student and teacher perspectives of using GenAI for various tasks in the writing process, including brainstorming, outlining, writing, and revising done by students, and evaluation and feedback done by teachers. The research questions guiding the study are:

1. 1.
What are undergraduate students' and teachers' perspectives on using GenAI in the writing process (brainstorming, outlining, writing, revising, evaluation, and feedback)?

2. 2.
How do student and teacher perspectives on the use of GenAI in the writing process compare?

Related literature

Writing instruction and assessment have made use of AI for some time in the form of programs like Grammarly or spell checker that identify grammatical and lexical errors in writing (Godwin-Jones, 2022). Yet, with recent advances in machine learning and natural language processing, AI-integrated language tools now pose a considerable challenge for educational systems that have relied heavily on writing to develop and assess the cognitive and linguistic competencies of learners. Students who do not use English as a first language can now use machine translation programs to accurately render their native language writing into a target language (Godwin-Jones, 2022); students can use programs like Quillbot or Wordtune to paraphrase, summarize, or adjust the tone of a paragraph (Yeo, 2023); and they can use ChatGPT to instantly generate entire essays. Authoring tools such as ChatGPT are prevalent and affordable (or even free) to anyone with an internet connection. ChatGPT garnered over 100 million users within three months of its launch (Hu, 2023), many of which are likely students and teachers. How GenAI, like ChatGPT, is being used in academic settings and how much reform will be required of educational systems as a result of this use is yet to be determined.

Looking at how individuals and institutions have used AI-integrated writing and language tools in the past might inform predictions of how GenAI tools like ChatGPT will be used in the future. For instance, automated essay scoring with AI has been the status quo for many large testing services, such as Pearson's Intelligent Essay Assessor and ETS's e-Rater (Gardner et al., 2020), which administer university placement exams, and other major exams such as the GRE and TOEFL. Receiving millions of summative writing samples each year, AI can greatly reduce the workload of manually scoring each essay (Hockley, 2018). However, the programs are typically limited to assessment of grammar, usage, mechanics, and style and are not able to detect more complex features like the presence of a thesis statement or overall coherence (Gardner et al., 2020). GenAI might be used to strengthen these tools to assess more complex discourse elements.

For formative writing, on the other hand, tools like Grammarly or Criterion are commonly used to provide corrective feedback during the revising and editing phases of the writing process. This includes basic grammar, spelling, and punctuation, or more advanced analytics such as word counts and readability (Fitria, 2021), all of which can be procured instantly and at any time. Multiple studies have researched automated writing evaluation (AWE) tools such as these with mixed results as to their efficacy and reliability (Huawei & Aryadoust, 2023; Wang & Han, 2022; Zhang, 2020), however, students tend to appreciate the feedback and respond positively to it (O'Neill & Russell, 2019). ChatGPT may be used as a cheaper and more robust AWE tool by students.

A recent experimental study by Fan ( 2023) looked at the effects and perceptions of university EFL students' use of Grammarly for corrective feedback on their writing. Although no differences were noticed in terms of writing improvement, most students in the Grammarly group found the feedback to be understandable and useful. For those that did not, Fan ( 2023) noted that students' low proficiency may have prevented them from effectively understanding Grammarly's feedback. One potential use of ChatGPT by EFL students is to revise commercial AWE feedback to be more comprehensible and accessible for them.

Although GenAI tools can be useful as a virtual tutor that offers individualized writing feedback, the threat of misuse sours these benefits. AWE tools like Grammarly and Criterion tend to be useful for evaluation and revision stages of the writing process, but GenAI tools like ChatGPT can further be used in the brainstorming, outlining, and writing stages of the writing process. The concern of authorship and plagiarism arises when students are using GenAI to give them ideas or to draft writing assignments (Ingley & Pack, 2023). This is the concern voiced by many in academia since the release of ChatGPT in November of 2022 (Sullivan et al., 2023).

Writing is a fundamental skill that is necessary for learner academic and professional development. In a joint publication, the Council of Writing Program Administrators, the National Council of Teachers of English, and the National Writing Project described the benefits of writing practices to develop rhetorical knowledge and critical thinking, which are in turn supported by habits of mind such as curiosity, creativity, persistence, and responsibility (CWPA, NCTE, & NWP, 2011). It has been shown that weak writing ability can result in less learning in all school subjects and negatively impact professional success (Graham, 2019; Graham et al., 2020). Therefore, it stands to reason that students outsourcing writing to AI will likely incur a negative impact on their cognitive ability and prospects for future success.

Concerns have also been voiced in how AI may be used by educators (Pack & Maloney, 2023a, b; Carlson et al., 2023; Lo, 2023). Educators have used GenAI to create course material and assessment tasks, adapt materials to be more suitable for specific students, and generate lecture notes (Bonner et al., 2023; Lo, 2023). However, one of the most frequently suggested uses of GenAI for teachers is to grade and provide feedback on student writing (Chiu et al., 2023; Kaplan & Haenlein, 2018; Weigle, 2013; Yeo, 2023). Accompanying some of these suggestions are characterizations of grading writing as burdensome and tedious, and GenAI is seen as a way of reducing teacher workload.

Teachers who seek to escape the 'tedious and burdensome' process of essay grading by using GenAI may inadvertently be signaling that it is ok for students to use the tool to "take the pain out of...the writing process" (Yeo, 2023, p. 2). In a position paper on machine essay scoring, the National Council of Teachers of English ( 2013) highlighted the social aspect of writing and that machine scoring sends students a message that writing is not worth the time because reading it is not worth the time. It will be imperative to incorporate GenAI in the writing process in a way that alleviates the "pain" and "burden" of the process without diminishing the social nature of writing.

Teachers might also be misled by the seeming impartiality of AI tools when the truth is these instruments are susceptible to influences unwittingly included in them by the developers who created them, a phenomenon known as algorithmic bias (Jackson, 2021). In educational contexts, algorithmic bias in GenAI can manifest when LLMs are trained on a convenience sample of language from the Internet which tends to be majority English language and majority western (Graham et al., 2015), resulting in underrepresentation of different languages, dialects, philosophies, ethnicities, and multiple other demographic divisors (Baker & Hawn, 2021). A study by Bridgeman et al. ( 2012), for example, showed that the e-Rater essay scoring AI produced inaccurate scores for populations along ethnic lines.

The use of GenAI for education involves other risks such as functional opacity, data privacy, and reliability (Yu & Guo, 2023) which need to be accounted for, but the usefulness of the technology for assisting in the writing process will likely lead to its adoption by teachers and students regardless of these limitations. To what degree do students and teachers feel it is acceptable to use GenAI in writing? Addressing this research question will provide a foundation from which common goals can be established that might assist with the successful adoption of GenAI in education.

Methodology

This study utilized a cross-sectional questionnaire design to make within- and between-group comparisons of students' and teachers' perceptions of the use of GenAI in learning and teaching.

Participants

A total of 226 participants (158 students and 68 teachers) completed the questionnaire. Students from across a variety of disciplines were recruited via non-probability voluntary response from a public research university in the United States with an incentive of course credit. Teachers were recruited using a purposeful sampling and snowballing method whereby the questionnaire was sent out to contacts of the authors at multiple institutions worldwide for distribution. The teacher questionnaire also provided a self-reflexive URL link for those who completed it with a request to forward the link to relevant individuals.

Second language teachers that teach a language different than the students' native language were the primary target of the teacher survey due to their experience with ground-up writing instruction and the corresponding need to address plagiarism and academic dishonesty regularly. Insights from English as a Second Language (ESL) and English as a Foreign Language (EFL) teachers may prove useful as they are exposed to non-western writing traditions which hold diverse views on intellectual property and originality (Pennycook, 1996; Sutherland-Smith, 2005). The student population was chosen for its homogeneity and typicality of public university undergraduates. A minority of surveys were collected from graduate students (n = 5) and non language educators (n = 3).

Informed consent from all participants was acquired and all ethical procedures were adhered to according to the standards of the university institutional review board. Participant demographics can be found in Tables 1 and 2.

Instrument

Divisions of use of AI in the writing process

A quantitative questionnaire instrument was developed to measure participants' perspectives on ways of including GenAI in the writing process. According to Seow ( 2002), process writing includes four basic stages: planning (including brainstorming and outlining), writing, and revising or editing. Planning includes prewriting activities that assist students in generating and organizing ideas. During the writing stage students focus on communicating their ideas to a specific audience in an initial draft. Based on feedback from the initial draft, revising occurs as students reexamine their writing and rewrite areas where their intent, style, tone, mechanics, or organization was identified as needing improvement. In addition to these steps that students complete, Seow highlights the importance of evaluation and feedback from teachers throughout the writing process. The questionnaire used in this study explored participants' perspectives on potential uses of GenAI for brainstorming, outlining, writing, revising, evaluating, and providing feedback.

For each of the writing process steps participants were asked to read an example GenAI prompt and response produced by OpenAI's ChatGPT (GPT-3.5 turbo) obtained in February of 2023. They were then presented with four divisions of use (or misuse) and asked to rate the appropriateness of each division on a five-point Likert scale (strongly disagree to strongly agree). These divisions and their explanations can be found in Table 3. The internal consistency of this instrument was measured with the alpha coefficient for each division of use of AI in the writing process. All alpha coefficients were above 0.7 which is considered satisfactory (Bland & Altman, 1997). These divisions of use were conceptualized by considering the common suggested uses of GenAI in recent literature (e.g., Kasneci et al., 2023), which include things like cognitive offloading of trivial tasks and idea generation, and also incorporating theory of plagiarism degrees of severity (Evering & Moorman, 2012; Yeo, 2007; Yeo & Chien, 2007), which differentiates plagiaristic behaviors.

Each division of use represented in the instrument was accompanied by an example of GenAI output that could be generated with simplistic prompting (using ChatGPT 3.5-turbo, February 2023 version). The simplistic approach taken when creating the examples was adopted to portray the capabilities of the technology from a layperson perspective, who may approach prompting naively. No sophisticated or iterative prompt engineering was used when generating the output for these examples but each prompt was submitted to ChatGPT separately so that the chatbot would not have a direct memory resource of previously submitted prompts. All complete prompts and output can be found in the appendix.

We divided the examples of GenAI use in the writing process between students and teachers so that brainstorming, outlining, writing, and revising were student actions and evaluating and providing feedback were teaching actions. In doing this we do not intend to suggest students cannot or should not use GenAI for evaluation or feedback purposes, but to demonstrate to participants that GenAI use in the writing process is not restricted to only students, and to elicit broader perceptions on the use of GenAI in education by multiple stakeholders.

Looking at the student-oriented prompts, for brainstorming we prompted ChatGPT to come up with ideas for an essay on the topic of urban challenges and global warming and the output provided 8 relevant ideas. The outlining prompt requested an outline for a 5-paragraph essay on the same topic and returned a bulleted outline including a thesis statement, paragraph topics, and supporting points. For the writing prompt we requested a fully written 5-paragraph essay on the same topic and it returned a coherent and cohesive essay. For revision we provided ChatGPT with a four-sentence paragraph that was written in an informal tone and asked for it to be revised to be more academic; the resulting output was more formal and academic.

As for the teacher-oriented prompts, we provided ChatGPT with a short paragraph that was replete with grammatical and lexical errors and prompted it to provide suggestions to the student on how to improve their writing. The output provided five somewhat generic suggestions for improvement along with examples, such as using transitional phrases, clearer language, and proofreading for errors. Last, we provided an error-free paragraph and prompted ChatGPT to evaluate the quality of ideas expressed therein. ChatGPT returned several sentences evaluating the argument made in the paragraph, commenting on development and logic.

Survey

Following the questionnaire section measuring perceptions of acceptable use, a short survey was included to better understand the sampled populations and to detect potential covariates or subgroups which might afford further analysis. These included eight survey items covering opinions about AI and technology in general measured on a five-point Likert scale (strongly disagree to strongly agree), and two to three (depending on group) yes/no response items (all items are listed in Table 7 in Sect. " Survey results").

To investigate the validity of the eight survey items, an exploratory factor analysis was conducted with all the completed surveys (n = 226) using principal axis factoring and a promax rotation method. The KMO test value was 0.674, showing an adequate proportion of variance in the survey which could indicate underlying factors. Also, Bartlett's Test of Sphericity was significant at the 0.001 alpha level. The analysis confirmed a four-factor solution (Table 4) which cumulatively accounted for 77.6% of the variance. However, factors 2 and 3 showed poor internal consistency, with alpha coefficients below the 0.7 benchmark. The low reliability of factors 2 and 3 is likely due to only having two items per factor and also disparate attitudes concerning teachers and students from the surveyed groups. Factor 1 can be described as the perceived utility of AI in education; factor 2 as perceived concern about AI in education; factor 3 as perceptions on technology change and innovation; and factor 4 as familiarity with AI.

Data collection and analysis

The questionnaire was created and distributed using Qualtrics Ⓡ . Questionnaire responses were collected during the spring semester of 2023. 15 incomplete questionnaires were discarded following the collection period.

Response frequencies were calculated for both groups on the levels of acceptable use of GenAI across each task and for the yes/no survey items. Mean responses and standard deviations were calculated for levels of acceptable use as well as the Likert-style survey items.

Although our teacher and student samples were disparate in size and population, exploratory comparisons were made to infer differences in perceptions. To do this, the Mann-Whitney U test was used to compare means for the teachers' and students' responses on the level of appropriateness of using GenAI, and also for mean responses to the survey items. A key assumption of the Mann-Whitney U test is independent observations from compared groups. This assumption was satisfied as participants completed the questionnaire individually and cross-group contamination was unlikely due to separate questionnaire hyperlinks.

Lastly, a principal component analysis was conducted to better understand teacher and student perceptions of GenAI use in the writing process across the multiple writing process steps and divisions of use.

Results

In general, both teachers and students held similar perceptions on what is appropriate use of GenAI in the writing process. That is, both groups predominantly agreed or disagreed along each division of use for each of the writing process tasks presented to them. Despite this general conformity, there were some significant differences in mean responses for some of the divisions of use as measured by the Mann-Whitney U test. Details of these response frequencies are provided in Table 5, and student-teacher comparisons are presented in subsequent subsections.

Brainstorming

Figure 1 shows response frequencies for teachers and students for the brainstorming task of the writing process. Students and teachers both generally felt that using GenAI to brainstorm ideas was acceptable if the student was already a competent brainstormer or only used the output as a model. Submitting AI-brainstormed ideas in class was seen as acceptable by half of teachers and students who took the survey, with another 10 to 16% uncertain and the remaining against. No significant differences were found between groups on these uses of GenAI. However, there was a larger difference between teachers and students when asked if it was ok to use GenAI to brainstorm ideas without disclosing the use of GenAI. Although teachers and students were predominantly in alignment in their disagreement that this use was acceptable, fewer students disagreed, and about 11% were uncertain. A Mann-Whitney U test comparing means between groups found a p-value of 0.032 ( U = 4491, r  = − 0.143) for division D on the brainstorming task.

Outlining

The outlining task resulted in more differences between student and teacher perceptions (Fig. 2). Means for division A of GenAI use (acceptable if the student is already a competent outliner) were not significantly different between groups, but for divisions B, C, and D significant differences were detected with students being more accepting of these uses. For using the GenAI output as a model, students were more accepting ( U = 3949.5, r = − 0.217, p = 0.001). For submitting GenAI output with disclosure ( U = 4488.5, r = − 0.134, p < 0.05) and without disclosure ( U = 3973.5, r = − 2.42, p  < 0.001) students were also more accepting than teachers.

Writing

Teachers and students had comparable perceptions on acceptable use of GenAI for writing an essay (Fig. 3) if the student was already competent in writing an essay (Division A pair) or if a student wanted to use a GenAI draft of an essay to model their own writing (Division B pair). Interestingly, both students and teachers predominantly viewed the use of GenAI for writing essays, even when the student is a competent writer, as inappropriate, but both students and teachers mostly agreed it was ok for students to use a GenAI generated essay as a model. Significant disagreement was found between teachers and students regarding submitting a GenAI-written essay with disclosure ( U = 4311.5, r = − 0.169, p = 0.011) and without disclosure ( U = 4245.5, r = − 0.223, p  < 0.001), with teachers more heavily disagreeing with this behavior.

Revision

Similar to writing, having GenAI revise an essay (Fig. 4) showed mixed perceptions between teachers and students with no differences detected for divisions A and B, but significant differences found in divisions C and D. Students were more accepting of using GenAI to revise their writing both with disclosure ( U = 4393, r = − 0.15, p < 0.05) and without disclosure ( U = 4030, r = − 0.23, p  < 0.001). However, like the writing task, the majority of both groups saw this as inappropriate use of AI.

Feedback

Feedback (Fig. 5) was framed as a teacher use of GenAI as an AWE tool. Non-significant differences in mean response frequencies were found along divisions A, C, and D, however, teachers agreed significantly more than students that division B was acceptable (using GenAI generated feedback as a model) ( U = 6397, r = − 0.157, p  < 0.05), although both groups were generally accepting of this behavior. Heavy disagreement was reported by both groups in using GenAI for providing writing feedback without disclosing the use of GenAI.

Evaluating

Using GenAI for evaluation of student writing was also framed as a teacher task (Fig. 6). Similar to the feedback task, significant differences on perceptions of acceptable use were only detected in division B (using the AI-generated evaluation as a model), again with teachers being more accepting of this use ( U = 6247, r = − 0.133, p  < 0.05). Again, both teachers and students felt it was inappropriate to use GenAI for this purpose without disclosing the use of GenAI.

Principal component analysis

In order to better understand how teachers and students perceive acceptable use of GenAI in the writing process a principal component analysis was conducted (Table 6). Dimension reduction was achieved with a varimax rotation specifying 3 factors, identified from components with an eigenvalue of 2 or greater on a scree plot, which accounted for 60% of the variance. The KMI measure of sampling adequacy value was 0.862 and Bartlett's Test of Sphericity was significant (p < 0.001). For variables with loadings in more than one factor, the smaller loading was suppressed.

Factor 1 included all of division D as well as writing, outlining, and revising for division C. These use examples all had the lowest mean agreement of acceptability (refer to Table 5) so we can label this factor as highly unacceptable use. Factor 2, on the other hand, primarily contained divisions of use A and B for outlining, revision, brainstorming, and writing. These use examples had relatively high means and were seen as generally permissible uses of GenAI, despite writing A having a majority disagreement response frequency. Factor 3 contained evaluation and feedback for all A, B and C divisions of use. The ratings for these use examples were mostly supportive, especially along the A and B divisions of use.

This analysis supports the validity of the instrument. Factor 1 contains the divisions of use that involve GenAI doing all the writing of a writing assignment, which was rated as the most unacceptable use of GenAI. Factor 2 contained the most acceptable uses of GenAI across four student-oriented steps of the writing process, which included utilizing GenAI for tasks that the user is already proficient in, and to generate ideas or model answers. The factor 3 use examples were categorized as being teacher-oriented, and their inclusion in one factor here demonstrates that participants conceptualized acceptable teacher use of GenAI differently.

Survey results

In addition to measuring perceptions on the use of GenAI in the writing process, we also included several survey items to measure other aspects of AI use in educational contexts (Table 7).

We asked questions to better understand perceptions about the utility of AI in education (items 1 through 3). There was a tendency to agree that AI would be useful to students and teachers in education, although some trepidation can be insinuated from relatively less agreement (and neutrality for students) to item 3, about AI having a positive impact on education. Some disagreement is evident between teachers and students for item 2, about the utility of AI for teachers, with a significantly higher percentage of teachers agreeing on AI's utility compared to students ( U = 7098, p  < 0.001).

Concern for student use of AI (items 4 and 5) was fairly high for both teachers and students, with slightly less concern for teacher use. Students showed significantly more concern regarding teacher use of AI than did teachers ( U = 4288.5, p  < 0.05).

We asked two questions to get a sense of participants' general feelings toward new technology and innovation (items 6 and 7). Teachers reported significantly more openness to the use of new technologies and innovative tools and methods in their teaching than did students for their learning ( U = 6441.5, p < 0.05; U = 3577.5, p  < 0.001).

Item 8 inquired about participant familiarity with AI. Mean familiarity scores were not high, but students reported slightly more familiarity with AI than did teachers, however the difference was non-significant.

We asked three yes/no questions (items 9 through 11; Table 8) about AI policy and preparedness. The student group (who all attend the same university) reported mixed answers when asked if their university had a policy on AI use. Teachers (who affiliated with various institutions), by comparison, expressed much more certainty that their institutions did not have an AI policy. Around 95% of both groups reported that they had received no training on the use of AI, and about 90% of teachers have not taught their students about the appropriate use of AI.

Discussion

This study sought to better understand how student and teachers perceive of the use of GenAI in the writing process within a framework of acceptability. The goal of this research is to contribute to the burgeoning discussion on how GenAI can be integrated into educational contexts successfully (see Godwin-Jones, 2022; Yeo, 2023). The prevailing narrative in our results demonstrated that for all the steps of the writing process, students and teachers generally agreed that using GenAI to brainstorm ideas or model answers, or as a form of cognitive offloading for tasks that the user is already competent in, is acceptable. Conversely, using GenAI to complete writing task assignments, with or without disclosing the use of GenAI, is unacceptable.

Both students and teachers perceived GenAI use to be more acceptable in the early stages of the writing process (i.e., brainstorming and outlining) than in later stages. These results suggest that use of GenAI for writing purposes is viewed as more acceptable when it is fulfilling a supportive role focused on idea generation and organization rather than when leveraged as an automatic writing completion tool. As to differences in perspectives, students tended to disagree less than teachers that using GenAI without disclosure was appropriate, and teachers tended to disagree less than students about using GenAI to model feedback or for evaluation of student writing.

The survey results (Tables 7, 8) further illuminated the findings on acceptable use of AI in educational contexts. Students and teacher both agreed that artificial intelligence would be a useful tool for teachers and students, but teachers tended to have a more positive outlook on teacher use of AI than did students. Yet both groups responded more cautiously when asked if AI would have a positive impact on education, and both groups reported concern about how AI might be used by teachers and students. The apparent trepidation regarding AI in education seems to be countered by the perceived utility of the tool. These are apprehensions that can be addressed by establishing clear policies on the use of AI and by educating both teachers and students on acceptable use.

Given the positive impact that university and classroom honor codes have on academic integrity by delimiting inappropriate practices (Ely et al., 2013; Konheim-Kalkstein et al., 2008), it is alarming that 94.1% of teachers reported their university as not having a policy in place regarding the use of AI and that 89.7% of teachers acknowledged they had never educated their students on acceptable use of AI. Clear university policies and statements on ethical use of GenAI are needed, such as the framework proposed by Chan ( 2023).

Additionally, teachers showed more openness to innovation, but 95.6% of teachers reported receiving no training on the use of AI from their institution. Many may be hesitant to embrace GenAI tools, such as ChatGPT, due to concerns related to cost, privacy, and legality (Kumar, 2023), in addition to a naivety as to how these tools can be appropriately used for educational purposes. Complicating this issue is the question of whether any prescriptions regarding the use of GenAI by students should also apply to teachers. Teacher training on these issues is urgently required.

The findings of this study underscore the importance of students and teachers being transparent in their use of GenAI tools. Using AI without disclosure, by both students and teachers, was considered the least acceptable practice. Yeo ( 2023) suggests that educators "accept and befriend [GenAI] by showing learners how to use AI authoring ethically and gainfully to achieve their learning intentions and goals" (p. 10). If educators are transparent and clearly state and model acceptable uses of GenAI then students may follow. While there is a nascent body of literature offering practical suggestions and advice for using GenAI in education by educators, learners, and researchers (Ingley & Pack, 2023; Pack & Maloney, 2023b; Bonner et al., 2023), an evidence-centered framework for leveraging GenAI in writing and in higher education in general needs further attention.

Conclusion

By the time this paper is published the GenAI tools and examples of use in this study will likely be anachronistic to a wider variety of available programs that users might interact with; a common limitation in emerging technology research. Even as this paper was being prepared, advances in prompt engineering have shown how students or educators might be able to use GenAI tools in ever more sophisticated ways. Regardless, the seemingly rudimentary prompts exemplified in this study represent how a teacher or student without much experience or training in prompt engineering would likely interact with a GenAI tool like ChatGPT. For this reason, the measured perceptions herein will likely be useful when considering approaches to GenAI integration in education.

Limitations of the study include the non-random sample which inhibits generalizing findings to a wider population, and the heterogeneous population of teachers and students that render between-group comparisons tenuous. Also, the cross-sectional design of the current study does not take into account the dynamic nature of student and teacher perceptions. It is reasonable to assume that both student and teacher perceptions of the ethics of using GenAI in writing education will change as individuals become more aware of and competent in the technology, and as the technology develops and changes over time.

While this research provides initial insights into student and teacher perspectives on using GenAI in academic writing, future studies could further examine potential differences in perceptions of students and educators in varying cultural and educational backgrounds, or take a longitudinal approach to see if opinions change as the technology becomes more prevalent. In addition, investigating stakeholder perceptions of the use of GenAI through more advanced prompts (such as having GenAI act as a teacher in a writing conference to guide a student through the writing process) would likely prove insightful.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Author information

Authors and Affiliations

1. College of Education, Florida State University, Stone Building, 114 West Call Street, Tallahassee, FL, 32306, USA

Alex Barrett

2. Faculty of Education and Social Work, Brigham Young University-Hawaii, 55-220 Kulanui Street, Laie, HI, 96762, USA

Austin Pack

Corresponding author

Correspondence to Alex Barrett.

Ethics declarations

Competing interests

The authors declare that they do not have any competing interests.

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Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

#1- Brainstorming

Student input:

Please brainstorm some ideas for an essay on the topic of urban challenges and global warming.

AI output:

1. 1.
Discuss the impact of increased urbanization on global warming and climate change.

2. 2.
Explore the role of city governments in mitigating the effects of global warming, such as implementing renewable energy sources and reducing greenhouse gas emissions.

3. 3.
Examine the challenges faced by cities in developing countries in addressing global warming and climate change, such as limited access to resources and funding.

4. 4.
Analyze the impact of global warming on urban ecosystems, such as heat islands and the loss of green space.

5. 5.
Discuss the ways in which urban residents can play a role in addressing global warming and reducing their carbon footprint.

6. 6.
Evaluate the effectiveness of existing solutions to urban challenges related to global warming, such as green buildings and public transportation.

7. 7.
Explore the potential long-term consequences of global warming for urban areas, such as sea level rise and extreme weather events.

8. 8.
Consider the potential benefits of addressing global warming in urban areas, such as improved air and water quality, and increased public health.

#2- Outline

Student input:

Write an outline of a 5-paragraph essay on the topic of urban challenges and global warming.

AI output:

1. I.
Introduction

2. II.
Briefly introduce the topic of global warming and its impact on urban areas.

3. III.
Thesis statement: Despite the challenges, addressing global warming in urban areas is essential for the health and sustainability of our cities.

4. IV.
Body Paragraph1: The impact of urbanization on global warming and climate change

5. V.
Discuss the increased greenhouse gas emissions resulting from urbanization

6. VI.
Explore the impact of heat islands on global warming

7. VII.
Mention the role of cities in contributing to global warming and climate change

8. VIII.
Body Paragraph 2: The role of city governments in addressing global warming

9. IX.
Discuss the ways in which city governments can mitigate the effects of global warming, such as implementing renewable energy sources and reducing greenhouse gas emissions

10. X.
Evaluate the effectiveness of existing solutions, such as green buildings and public transportation

11. XI.
Body Paragraph 3: The challenges faced by cities in developing countries in addressing global warming

12. XII.
Explore the limited access to resources and funding faced by cities in developing countries

13. XIII.
Discuss the potential consequences of global warming for these cities, such as sea level rise and extreme weather events

14. XIV.
Conclusion

15. XV.
Summarize the main points made in the essay

16. XVI.
Restate the thesis

17. XVII.
Call to action: urge readers to consider the importance of addressing global warming in urban areas and take action to reduce their carbon footprint.

#3- Writing

Student input:

Write a 5-paragraph essay on the topic of urban challenges and global warming.

AI output:

Urban areas around the world are facing a range of challenges related to global warming and climate change. The increased greenhouse gas emissions resulting from urbanization contribute to global warming and exacerbate the impacts of climate change. Additionally, the heat generated by urban areas can create heat islands, which further contribute to global warming.

City governments have an important role to play in addressing global warming in urban areas. They can implement policies and programs aimed at reducing greenhouse gas emissions, such as promoting the use of renewable energy sources and increasing energy efficiency. They can also invest in infrastructure that supports sustainable transportation, such as public transit and bike lanes.

However, cities in developing countries often face challenges in addressing global warming and climate change. They may have limited access to resources and funding, making it difficult to implement solutions to mitigate the effects of global warming. These cities may also be more vulnerable to the impacts of global warming, such as sea level rise and extreme weather events.

Despite these challenges, addressing global warming in urban areas is essential for the health and sustainability of our cities. By taking action to reduce greenhouse gas emissions and adapt to the impacts of global warming, we can protect our urban environments and ensure that they remain livable for future generations.

In conclusion, global warming and climate change present significant challenges for urban areas. City governments have an important role to play in addressing these challenges, but they must also overcome obstacles such as limited resources and funding. By taking action to reduce greenhouse gas emissions and adapt to the impacts of global warming, we can ensure that our cities remain sustainable and livable for the future.

#4- Revision

Student input:

Rewrite the following paragraph and make it sound more academic:

Global warming is important for several reasons. First, global warming will affect many people. All countries and their people will be affected by global warming. Second, the effects of global warming will be costly. Third, many people will die because of global warming.

AI output:

The significance of global warming is multifaceted. Its impacts will be far-reaching, affecting individuals and communities across all nations. Additionally, the economic costs of global warming will be significant. Furthermore, it is anticipated that global warming will result in a significant loss of human life.

#5- Feedback

Teacher input:

Can you provide some suggestions on how to improve this student's writing:

Culture shock is a feeling of disorient that occur when somone is in a new culture. It can be caused by factors such as differences in language, culture, and ideas and culture shock include homesickness, difficulty eat to new foods, and feeling of isolation or depression and it is important to recognize that culture shock is normal part of adjusting to new culture. And it is possible to overcome it with time and effort. Someway to cope with culture shock is connect with friends and family back home, and get out support from others who also have same feelings. By choose to learn about and adapt to a new culture, individuals can slowly learn enjoy and appreciate new culture.

AI output:

Here are some suggestions for improving the writing:

1. 1.
Use clear and concise language: Avoid using long or complex sentences, and try to use straightforward language.

2. 2.
Use transitions: Add transitional phrases or words (e.g., "for example," "however," "in addition") to help the reader follow the flow of the writing.

3. 3.
Use concrete examples: Provide specific examples to illustrate the points you are making. This will help the reader understand and relate to your writing more easily.

4. 4.
Use active voice: Write in the active voice (e.g., "She overcame culture shock by learning about the new culture") rather than the passive voice (e.g., "Culture shock was overcome by her learning about the new culture"). The active voice makes the writing more engaging and easier to follow.

5. 5.
Use proper grammar and punctuation: Review your writing for errors in grammar and punctuation, and correct them as needed. This will help ensure that your writing is clear and easy to understand.

#6- Evaluate

Teacher input:

Evaluate the quality of ideas expressed in the following students' paragraph:

I believe arming teachers with guns will make the classroom safer. To begin with, the only way to stop a bad guy with a gun is a good guy with a gun. Teachers are good people and people trust them. Students and teachers can feel safer if the teacher has a gun. Second, having a gun is protected by the government. The reason why this is protected is because guns can protect people from dangerous people and governments. Therefore, teachers with guns will make the classroom safer.

AI output:

While the paragraph presents some ideas about arming teachers with guns, the reasoning and logic used to support these ideas are problematic and flawed. The first point made in the paragraph relies on the premise that the only way to stop a bad person with a gun is to have a good person with a gun, which is not necessarily accurate or supported by evidence. The second point asserts that having a gun is protected by the government, but does not provide any context or explanation for this claim. Additionally, the final statement that teachers with guns will make the classroom safer is not supported by any evidence or logical reasoning. Overall, the ideas expressed in this paragraph are not well-developed or thoughtfully presented and do not effectively convey a coherent or logical argument.

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