Artificial Intelligence in Education

Content 1. Introduction 2. Institutional strategies for the use of artificial intelligence (AI) 3. Applications in the teaching-learning process: 3.1 Content design 3.2 In the classroom 3.3 Evaluation 4. Activities incorporating AI technology 5. Most used AI tools 6. Use cases of AI in education 7. References

1. Introduction Artificial intelligence (AI) has become a transformative force in contemporary society, revolutionizing a wide range of industries and sectors. In higher education, institutions are beginning to recognize the potential of AI to improve the quality of teaching, research, and institutional management. Effective implementation of artificial intelligence in educational institutions can not only drive innovation, but also increase the efficiency and effectiveness of university operations.

Most experts agree that generative AI is here to stay, and it is worthwhile for higher education leaders to think now about how they can interact with it to protect student privacy, improve the student experience, and help all stakeholders be prepared for a future of working with powerful technology (Cohn,2023). For some teachers, that is reason enough to incorporate it into teaching: it is better to show students how to use it effectively and understand its limitations than to ignore it (McMurtrie,2023).

This document presents an analysis of information from different sources on the applications of Artificial Intelligence in Education: institutional strategies, tools used, applications and activities that incorporate this technology, seeking to provide valuable information for its strategic application in the university context.

2. Institutional strategies for the use of artificial intelligence (AI) According to Shamkina (2023), in her publication "AI in Education: Top Applications, Real-Life Examples, and Adoptions Tips," the successful implementation of AI in the education market requires careful consideration and strategic planning. To achieve this, she proposes to approach it through three main topics: 1. Exploration: First, it is important to understand the core elements of AI-based approaches and how they can be used in educational settings. This includes examining the use cases and applications of Machine Learning, Natural Learning Processing, and deep learning. Next, it is important to assess the current state of AI-based solutions in educational contexts. This includes analyzing the availability of products and services that can be incorporated into an AI-based approach, as well as the level of sophistication achieved by the most advanced schools and

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universities using these technologies. In addition, it is important to understand the legal and ethical implications of implementing AI-based systems in educational environments.

2. Planning: The next step is to develop a comprehensive plan for implementing AI-based solutions in the educational institution in question. This includes defining specific objectives, establishing appropriate metrics and goals, and developing a project timeline. In addition, the skills, resources, and technologies needed to build an AI-based system should be identified. Consideration should also be given to the most appropriate vendors or organizations that provide these services, as well as creating a budget for their development.

3. Monitoring: Finally, it is essential to establish a governance structure for AI-based solutions in educational environments, which involves creating a set of guidelines and protocols to ensure that the AI-based system complies with all applicable laws and regulations. In addition, it is required to create a process to monitor the use of AI-based solutions in the education sector and ensure that data privacy is maintained throughout the implementation process.

According to Nguyen (2023), in his publication "AI in Higher Education: 8 Key Strategies for Institutional Leaders", Sharma et al (2022), Wang et al (2021), and Rico-Bautista et al (2021), institutional leaders should consider the following points for AI adoption: 1. Recognizing the power and potential of AI One of the big misconceptions institutions often make is to dismiss AI as just another passing trend. As AI becomes integrated into various tools and platforms to support and accelerate workflow, productivity and more, it's hard to ignore its significant impact on higher education.

2. Redesign policies to accommodate AI The rise of AI has raised concerns about academic dishonesty, and institutions fear that students will become passive thinkers and rely heavily on AI tools to complete their work. Plagiarism detection platforms are seen as useful solutions that enable faculties to detect AI-generated content and prevent cheating.

3. Support faculty in the AI domain Another critical factor for a smooth adoption of AI is to provide training to staff. Sufficient AI knowledge and skills will make it easier for faculty to integrate the technology into their curriculum, while guiding and supporting students to use AI appropriately.

4. Increase productivity in administrative tasks with AI ● Support the admission process: AI tools can perform predictive analytics, allowing institutions to predict students most likely to be admitted and better allocate financial aid to promising candidates. ● Manage student progress: by using AI predictive analytics, institutions can cultivate a holistic and inclusive assessment environment where student performance is not measured by numerical grades alone. ● Reduce dropout rates: schools can identify in advance which students are at risk of dropping out and provide personalized instruction and support to improve learning experiences.

5. Integrate AI into curriculum development In addition to curricular updates, institutions must transform the way students are assessed in response to the increasing use of AI. This is the opportunity for faculty to invest more in alternative assessment methods, such as authentic assessment, programmatic assessment, and more.

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6. Supporting personalized learning To ensure a meaningful learning experience for students, timely and efficient support is critical. AI also demonstrates capabilities to be a personal tutor for students, guiding them to give feedback, explain key concepts or highlight possibilities for improvement.

Ethan Mollick and Lilach Mollick of the University of Pennsylvania Wharton School propose seven approaches for using AI in the classroom as a supportive rather than a replacement tool, e.g. ● ● ● ● ● ● ●

AI-tutor: This approach uses AI to increase knowledge by providing personalized instruction and feedback to students.

AI-coach: This approach uses AI to increase metacognition by helping students develop self-awareness and self-regulation skills.

AI-mentor: This approach uses AI to provide balanced, ongoing feedback to students, helping them to identify areas of strength and weakness.

AI-teammate: This approach uses AI to increase collaborative intelligence by facilitating group work and communication among students.

AI-tool: This approach uses AI to extend student performance by providing tools and resources that enhance learning and problem-solving.

AI-simulator: This approach uses AI to help with practice by providing simulations and scenarios that allow students to apply their knowledge and skills in a safe and controlled environment.

AI-student: This approach uses AI to check for understanding by assessing student learning and providing feedback in real-time.

An example of the application of these strategies is found in Sal Khan's presentation where he shows new functionalities of the educational chatbot, Khanmigo, from Khan Academy.

7. Preparing students for the world of artificial intelligence Undoubtedly, the demand for AI skills will only increase in the future as companies realize the benefits that both AI and the ability to use this technology can offer. AI literacy, therefore, should be considered a key competency that students should develop throughout their learning. The skills involved in literacy are: ● ●

Organizing AI courses and providing resources are effective ways to cultivate a solid understanding of technology among students.

Effective use of AI to complete different tasks. Students receive detailed instructions on how to adopt the technology and, at the same time, are motivated to explore the tools on their own.

8. Encourage open conversations about AI This effort and its impact can be multiplied with the support of institutional leaders. Open conversations about AI, in which faculty, staff, and students engage in the exchange of opinions and ideas for adopting AI, should be significantly encouraged at both the departmental and institutional levels.

9. Consult with experts Once the needs of the institution have been identified, universities should consult with technology experts to plan how best to address them using AI systems. At this stage, a team of experts with the necessary skills should be in place who will oversee the implementation process and select the right partners for the university to work with. When selecting a vendor, it is important for universities to understand what they offer, how their systems integrate with existing infrastructure, and the adjustments needed for use, all while meeting the needs of the university (Sharma et al, 2022).

10. Train teachers and university staff

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This allows them to effectively use these tools effectively and efficiently in their educational practices. In addition, training faculty and staff on what to expect from AI in higher education can alleviate concerns that AI will make their jobs obsolete and replace them. Therefore, providing hands-on training that focuses on the pedagogical use of AI, emphasizing how it will enhance education and research, and fostering the perceived usefulness of AI technologies will give faculty the motivation to adopt the technologies more readily (Wang et al, 2021; Rico-Bautista et al, 2021; Sharma et al, 2022).

11. Enabling infrastructure Classrooms must be equipped with the right resources to ensure that AI can be harnessed to its full potential and provide an accessible learning environment for students who might not otherwise be able to access such resources on their own. Beyond the classroom, it is also important to provide spaces for students to use technology; libraries, study rooms, and learning commons are some examples. These spaces should be provided for students, faculty, and university employees alike (Miranda et al, 2021).

Most importantly, digital infrastructures must be in place for AI to be better adopted. It is important to analyze both pros and cons and make a final decision on whether implementation is feasible. Key factors to consider include the ability of the institution's infrastructure to handle current and future data traffic (Rico-Bautista et al, 2021).

12. Developing an ethical framework As AI applications continue to develop and rapidly integrate into widely used products, using AI applications in the university context is inevitable. Rather than implementing restrictive policies, universities and educators should focus on promoting responsible use and addressing potential challenges associated with AI tools (Gimpel et al, 2023).

How data is handled, and the security measures implemented directly impact the level of trust placed in the technology. Universities must demonstrate accountability by developing data systems in an ethical manner, showing care in the use, processing, and sharing of data, while protecting people's data (Sharma et al, 2022; Rico-Bautista et al, 2021).

Transparency is also key to developing an ethical framework. Building trust and encouraging adoption of AI systems requires effective communication. Universities should communicate the purposes of AI systems and their benefits to citizens through various channels, such as email, websites, social networks, and other collaborators (Sharma et al, 2022).

13. Define learning objectives For professors, before using any AI tool as a teaching tool, it is crucial to define the specific learning objectives of their courses. Higher education learning objectives can vary by field of study and subject matter. The ability of professors to create and refine prompts tailored to the desired tasks or objectives is essential to achieve the desired outcomes (Gimpel et al, 2023).

3. Applications in the teaching-learning process Within the "AI+Education" summit organized in February 2023 by the Human-Centered Artificial Intelligence Institute at Stanford University, which brought together researchers, entrepreneurs and experts in artificial intelligence and education to explore the potential of AI in improving teaching and learning processes, and the research conducted by Fernando Vera of the University of the Basque Country entitled "Integration of Artificial Intelligence in Higher Education: Challenges and Opportunities” the following applications for use in education were highlighted:

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● ● ● ● ● ● ● ● ● ● ● ● ●

Improve student participation and engagement in the classroom.

Improve personalized support.

Stimulate student creativity and critical thinking.

Shift the focus for students.

Improve accessibility of content for students with disabilities.

Expand access to resources and knowledge.

Adapt to online or hybrid learning environments.

Improve the quality of learning and assessment.

Meet the demand for digital and technological skills in education.

Streamline and improve the efficiency of educational tasks.

Strengthen language learning in a playful way.

Complement traditional teaching with innovative approaches.

Preparing students for an increasingly digital and technological world

An analysis was carried out on 60 papers on the uses of artificial intelligence in higher education institutions published in Scopus during 2022 and 2023. 46 articles were analyzed to identify their objective, tool used, as well as their use case. 14 articles whose approach did not apply to this analysis were discarded. They were categorized based on the approaches of the Artificial Intelligence Strategy of the Undergraduate and Graduate Rectorate 2023.

Fig. 1. List of institutional strategies, uses and tools of AI in higher education

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The application rate of the approaches is broken down as follows: ●

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1. Learning with AI tools in Teaching & Learning (T&L) represents 67% of the initiatives implemented such as designing solutions with AI tools (16), use of T&L enablers with AI tools (12).

3. Learning about AI technologies and techniques (in disciplines and research) represents 22% with nine papers on linking research projects on AI and one paper about opportunities in careers and programs.

2. Prepare students, teachers and collaborators so that they understand the effect that AI can have on human life and the ethical aspects to address with one paper.

Four papers were identified as School management related to admissions processes and cybersecurity.

3.1 Teaching and learning process: Content design The technological change we are experiencing invites universities to reevaluate the content and delivery of their courses, as well as their assessment methods. Educational experts and researchers present some recommendations for the use of AI when designing content.

Fernando Vera (2023) of the University of Basque Country proposes that the following should be taken into account when creating courses integrating AI (Figure 2):

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Co-creation: collaborate with students in co-creating innovative approaches that ensure meaningful assessment and learning outcomes. It could offer a pathway to understanding risks and impacts on all parties. The human values of connection, collaboration, and sharing best practices are paths to follow in times of uncertainty (Dixon, 2023).

Include socioemotional aspects in AI-based activities: These are essential to ensure a holistic and comprehensive approach to student learning. It is important to recognize that education is not only about acquiring knowledge, but also about developing socioemotional skills and emotional well-being (Vera, 2023).

Include ethical aspects in AI-based activities: It is essential to ensure that its implementation is responsible and benefits all those involved. AI has great potential to transform the way learning and teaching takes place, but it also raises ethical challenges and concerns in terms of privacy, bias, fairness, and transparency (Vera, 2023).

Incorporate AI tools into existing course content and objectives while ensuring that they are aligned with learning outcomes (Hong Kong University, 2023).

Incorporate AI literacy into the common core curriculum, including courses on the history of AI, its impact on society, and its potential to shape the future (Hong Kong University, 2023).

3.2 Teaching and learning process: in the classroom Authors such as Andreas Breiter, professor and Chief Digital Officer at the University of Bremen in Germany, Priten Shah, author and technology-focused educational entrepreneur, and Fernando Vera, from the University of the Basque Country have focused on the use of AI in the classroom, where they state that: ●

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Encourage a critical, reflective, and analytical approach to AI, including interpretation and evaluation of AI-generated content and outcomes. Critical thinking about AI must be a central part of students' experiences so that they are prepared for the multimodal learning experiences they will continue to encounter beyond the higher education environment (Breiter, 2023).

Faculty in all disciplines must be receptive to the changes that AI will bring, to understand how work is done in their fields, and also how students can engage in critical thinking about AI (Breiter, 2023).

Classrooms will need to be much livelier than before. Instead of writing an essay, they may be asked to present a case study and asked to research the topic in any way, including AI. The same applies to preparing for a role play: pretending to be on an educational board, a policy maker, or in a business boardroom: "You have to use evidence from what you've learned. You have to think critically...it's a lot more fun for students [than writing essays]" (Shah, 2023).

AI can facilitate communication, teamwork, organization, and task planning, enabling students to work together efficiently and effectively (The Hong Kong University, 2023).

AI can analyze how teamwork is observed in students, identifying roles and group dynamics, and providing feedback on how to improve collaboration and team effectiveness (Vera, 2023).

Encourage active student participation in the learning process. For example, through online collaboration tools or automatic feedback, students can be encouraged to actively participate in classes and interact with the content (Vera, 2023).

Encourage creativity and innovation in both students and teachers. For example, through AI-based content generation tools, creativity can be stimulated and innovative ideas for projects and assignments can be generated (Vera, 2023).

Develop students' AI literacy: Highlight the importance of developing AI literacy among students, including an understanding of algorithms, data analysis, and the ethical and social implications of AI (The Hong Kong University, 2023).

Provide students with hands-on experience with AI technologies and platforms, such as machine learning frameworks, natural language processing tools, and generative systems. This will help

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students gain a deeper understanding of AI and its potential applications and develop practical skills that will be valuable in the workforce of the future (The Hong Kong University, 2023).

Create a collaborative environment where students from different disciplines can work together to develop solutions to real-world problems using AI. This will help students understand the interdisciplinary nature of AI and how it intersects with fields such as psychology, sociology, and ethics. By working on projects that apply AI to real-world problems, students will gain a deeper understanding of the potential of AI and the importance of responsible and ethical use (The Hong Kong University, 2023).

3.3 Teaching and learning process: Evaluation Another opportunity AI brings to education is the potential to improve the way student learning is assessed.

Through artificial intelligence, institutions can apply assessments to students in a more automated, secure and efficient manner. In this way, it is also easier for the teacher to grade with a high degree of accuracy.

Beyond using a program that produces a numerical result, we are talking about intelligent systems that can evaluate, detect anomalies, provide statistics, and even make evaluative calculations.

However, according to Gallardo (2021) in “Evaluation: Innovations in educational assessment”, he mentions three important points to address in the topic of learning assessment using AI: ●

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The first is about the incursion of this technology into feedback. Advances are reported in the use of learning analytics to provide personalized guidance to individual learners. This process becomes especially useful when there is the option of analyzing large learning databases, as, for example, in large groups, and MOOC offerings. It is precisely in these environments, where it becomes very costly to give person-to-person feedback to thousands of learners taking these courses from different locations around the globe.

The second is also found in feedback, but of specific processes such as text writing. AI currently integrates patterns that occur in written communication, which has made it possible to issue a series of criteria based on which students can receive feedback on their written texts, ranging from scriptural elements (vocabulary, syntax, grammatical elements, formality, use of language) to emotional aspects (possible reader reactions, feelings conveyed, etc.)

The third goes beyond the product generated, be it test answers, essay writing or learning activities. This third AI contribution focuses on the recording of timestamps, movements and expressions that are generated each time our students develop learning activities. This type of record could even indicate how they solved a problem, the mistakes made and those they could potentially make, the misapplication of concepts and even their ability to recover from the frustration of little or no progress in their learning.

According to the Educational Innovation Lab Escuela21, there are seven advantages of the application of AI in learning assessment: ●

Advantage 1: AI can help reduce bureaucracy.

It can help to minimize the time dedicated to correction by teachers and facilitate the management of the recording and monitoring of evidence of learning, encouraging this time to be dedicated to the development of strategies to accompany students in their development. ●

Advantage 2: AI can make assessments more inclusive.

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Digital integration and the use of AI will contribute to increase the possibilities of improving accessibility, as well as diversifying the forms and times of assessment. ●

Advantage 3: By applying AI we can achieve an integrated assessment.

A continuous and background assessment, supported by AI, will reduce the prominence of assessment tests, and will achieve an authentic and infused assessment, reducing the saturation of assessment tests. ● Advantage 4: AI enables authentic assessment of learning.

The fact that the assessment is integrated in real activity processes will reduce the test-preparation effect in the teaching processes. ●

Advantage 5: AI allows us to give personalized feedback to each student.

Access to real-time information on student progress can facilitate individualized feedback, giving a more formative character to the assessment, providing feedback in time to be applied by the student to improve their learning process. ●

Advantage 6: AI allows giving students proactive feedback.

The AI makes it possible for this to be proactive feedback focused on the accompaniment for decision making on how to continue advancing in the learning process. ●

Advantage 7: AI enables long-term monitoring of learning.

AI can enable teachers to monitor students over longer periods of time than the school year and help teachers understand learning styles, identify difficulties and manage the support needed to reduce barriers to learning.

An important point to consider are the existing tools for evaluating learning using AI such as: ●

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Online assessment platforms: There are online platforms that integrate AI to create and manage interactive assessments. These platforms can generate adaptive questions, automatically correct answers, and provide personalized feedback to students.

Writing analysis systems: These systems use AI to analyze and evaluate the quality of content written by students. They can detect aspects such as grammar, coherence, style and originality.

These tools are useful for evaluating essays, reports, and other written work.

Programming assessment tools: AI is used to evaluate code written by students in programming languages. These tools can verify the correctness and efficiency of the code, as well as provide feedback and suggestions to improve it.

Plagiarism detection systems: These tools use AI to compare the content of students' work with a large database of existing online resources. They can identify similarities and possible cases of plagiarism, helping educators maintain academic integrity.

Virtual assessment assistants: Some tools use AI to simulate interaction with a virtual teacher or tutor. These assistants can ask questions, evaluate answers, provide feedback, and even adapt their approach based on students' individual needs.

Assessment data analysis tools: AI is also used to analyze large sets of assessment data and extract meaningful insights. These tools can identify patterns, trends, and areas for improvement, allowing educators to make more informed decisions about their teaching.

Using AI in evaluation must involve its design. The “Guide for the use of Artificial Intelligence tools in Higher Education” published by The Hong Kong University of Science and Technology in February 2023, through the Center for Education Innovation, offers the following recommendations for the design of evaluations using AI:

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Use AI tools for formative assessment, such as pre-tests, progress tracking, and self-assessments.

Design assessments that require students to analyze, evaluate, and interpret AI-generated content, rather than simply recycle it.

Use AI tools to suggest assessments that can be mapped to the expected learning outcomes (LOs) of the course in question.

Encourage students to reflect and take note of the AI-generated feedback on their work.

Use AI-generated feedback to provide consistent and objective feedback but pay attention to its limitations.

Provide real-time feedback, allowing faculty to make data-driven decisions and make necessary adjustments to their teaching and assessment strategies. Train instructors to provide effective feedback on AI-generated content and engage learners in developing critical thinking skills.

Develop a clear and transparent rating system for AI-generated content that takes into account factors such as originality, accuracy, and critical thinking skills.

Create adaptive assessments by automatically adjusting the level of difficulty based on a student's performance.

4. Activities incorporating AI technology In recent years, we have witnessed a rise in the application of AI in higher education. Researchers and educators have seen that AI is used in various ways to improve teaching and learning experiences (Acharya, 2023). Some notable examples include: ● ● ● ●

Adaptive learning platforms: These personalized systems use algorithms to analyze student performance and adapt content to their specific needs.

AI-Enabled Tutors: Virtual assistants can provide immediate feedback to students, answer questions, and clarify concepts.

Smart assessment tools: AI-powered technologies can assess assignments, exams, and even essays, alleviating some of the workload for teachers.

Learning Analytics: AI can analyze institutional databases, course materials, and student interaction patterns, providing insights to improve learning outcomes.

Today, there are numerous applications of AI systems. Below are some of the most common use cases: ●

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Speech Recognition: Also known as Automatic Speech Recognition (ASR), computer speech recognition, or speech-to-text, it is a capability that uses natural language processing (NLP) to process human speech into a written format. Many mobile devices incorporate voice recognition into their systems to perform voice searches, e.g., Siri, or provide more accessibility to text messages.

Computer Vision: This AI technology allows computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and based on those inputs, they can take action. This ability to provide recommendations distinguishes it from image recognition tasks.

Recommendation engines: Using data from past consumer behavior, AI algorithms can help uncover data trends that can be used to develop more effective strategies. This is used to make relevant plugin recommendations to users during the process.

5. Most used AI tools AI-based tools are transforming higher education, offering innovative solutions to improve teaching, learning and the student experience. HolonIQ identifies four key technologies that drive the use of AI in education: vision, voice, language and analytics.

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Analytics and language are currently perceived to have the greatest potential impact on the education industry, particularly in scaling and integrating smart adaptive learning solutions across the education sector. Voice and vision are also important features for intelligent adaptive learning systems and are expected to have a significant impact on the education sector as they are developed and integrated into teaching (Figure 3).

Likewise, the impact of AI technologies in the educational sector can be seen in Figure 4:

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Below are the AI technologies that are being used in education: Category Support to the institution

Tool Dream Apply

Teaching support Learning support

Learning Studio AI Replit

Learning support

Perplexity ai

Learning support

AISEO

Support to the institution

Mutiny

Teaching support

Capsule video

Teaching support

Runway ML

Description It is a video student admission management system, designed with and for educational institutions.

Online course creation tool.

It is a software as a service (SaaS) that allows users to create online projects and write cod.

Search engine that integrates conversational AI technology and provides concise, simple and up-to-date answers linked to reliable sources.

AI-powered writing tool.

The writing assistant allows you to experiment with: writing essays and reports.

Convert more leads by targeting unique audiences on each site and displaying the most relevant version of content without the need for engineers or data scientists.

Tool for collaborative editing of videos that allows the creation and rendering of videos with professional quality.

It is software intended primarily for graphic designers that produces visual content, such as animations, interactive designs, images, etc.

Use Admissions

URL https://www.dreamap ply.com/

Course design

https://learningstudioa i.com/ https://replit.com/

Programming

Search

https://www.perplexity .ai/

Drafting

https://aiseo.ai/

Marketing

https://www.mutinyhq. com/

Educational videos

https://capsule.video

Design

https://runwayml.com/

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Learning assessment technologies using AI Name Gradescope

MI Write

Thinkster Math

Brainly

Alta by Knewton

GitHub Copilot

GPTZero

Originality by Turnitin

Cognii

Description It makes evaluations much faster for instructors. Students upload their exams to the platform, and its AI capability sorts and groups answers for verification.

According to Gradescope, their use of AI decreases time spent grading by 70 percent or more.

Automated writing assessment program that provides automated scoring and feedback on student writing. Automated writing quality scores can be used to examine changes in performance over time, and automated feedback helps students improve their knowledge of writing quality criteria.

It uses AI and machine learning to follow the steps students take when solving math problems. Students solve problems in the application and produce detailed progress reports that specify their understanding of the different skills assessed.

It is not used in the classroom. Rather, online chat makes learning easier for students to ask questions, connect with “friends,” and have their questions answered by other students. Brainly moderators check the questions and answers on the platform to ensure they are of high quality. The company also developed machine learning algorithms that automatically filter out spam and low-quality content, such as incorrect answers, so that moderators have more time to focus on helping students navigate the site.

Adaptive learning technology is at the heart of Alta's personalized learning experience. It is a complete courseware solution that combines Knewton's expertly designed adaptive learning technology with high-quality, openly available content to deliver a personalized learning experience that is affordable, accessible and improves student outcomes.

It is an AI pair programmer that helps write code faster and with less work.

Extracts context from comments and code to suggest individual lines and entire functions instantly. GitHub Copilot is powered by OpenAI Codex, a generative pre-trained language model created by OpenAI. Trained on billions of lines of code, GitHub Copilot turns natural language cues into coding suggestions in dozens of languages.

It is a classification model that predicts whether a document was written by a language model, providing predictions at the sentence, paragraph, and document levels. GPTZero was trained on a large and diverse corpus of human-written and AI-generated text, with a focus on English prose. GPTZero works robustly on a variety of AI language models, including, but not limited to, ChatGPT, GPT-3, GPT-2, LLaMA, and AI services based on those models.

Address the originality of student work and emerging trends of misconduct with this comprehensive solution. Through panels (dashboards) they help identify risks and perform analysis. Reports show results within the context of students' tasks. Check similarity with your content database. Reveals text manipulations intended to bypass integrity checks. Students can check text similarity and grammar before submitting. The citation assistant finds missing citations and teaches the appropriate citation style.

It combines the powers of conversational pedagogy with conversational artificial intelligence technology. It engages users in a chatbot-style learning conversation by asking it to construct a response, providing an instant

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Carnegie Learning

StepWise by Querium

Educator Lab

Conker

EdPuzzle

Socrative

Wolfram Alpha

formative assessment, instructing it with personalized hints and tips, and guiding it toward conceptual mastery.

It leverages machine learning algorithms to provide students with interactive lessons and real-time feedback, helping them understand difficult concepts and improve their skills. The software offers various learning solutions in subjects such as mathematics, literacy, and world languages. It is designed to provide personalized student support and insightful data. Teachers can also provide real-time feedback and assessments, allowing students to understand where they excel and where improvements are needed.

Mimicking the guidance and motivation of an experienced teacher, students solve problems by submitting each step for evaluation and receive immediate feedback, including mistakes made and suggestions for next steps. It is adaptive and personalized, generating an individual learning path for each student and subsequently adapting to the student's progress so that they advance as soon as they are ready, providing valuable data to instructors to give them insight into problem solving and level of students' ability.

SaaS (Software as a Service) tool to generate AI-enabled lesson plans, worksheets, and activities. With it you can: Customize lesson plans, worksheets, and activities to meet the needs of your students and the curriculum. Access an extensive library of educational resources, including standards-aligned content and multimedia resources. Save time and reduce stress by automating the lesson planning and assessment process.

It is a tool that allows users to create quizzes and formative assessments easily and quickly. The tool allows users to create multiple choice quizzes with a specific number of questions for different grade levels. The tool is designed to use machine learning algorithms to analyze user data and improve the functionality of the platform.

Tool used to convert any video into an interactive class. With EdPuzzle you can edit the video, add an audio track to explain it, include a test or open questions. It allows you to know the retention of content in real time. Formative assessment tool for teachers managing a flipped classroom, assigning videos for homework, or wanting to encourage asynchronous communication. The teacher can also have students use EdPuzzle to show whether or not they achieved the goals at the end of the semester.

Stimulates higher learning by tracking students' real-time understanding. Turn every lecture into a two-way exchange with the app providing immediate feedback on teaching. Quizzes, surveys, team activities, and content from educators around the world, all in one easy-to-use assessment tool. The Socrative app is free for all students and non-administrators.

The introduction of Wolfram|Alpha defined a fundamentally new paradigm for obtaining knowledge and answers, not through web searches, but through dynamic calculations based on a vast collection of data, algorithms, and integrated methods. Providing broad, deep, expert-level knowledge to everyone... anytime, anywhere. The long-term goal of Wolfram|Alpha is to make all systematic knowledge immediately computable and accessible to everyone.

6. Use cases of AI in education a. General education By leveraging AI-based technology, educational institutions can unlock new opportunities for their students, teachers, and collaborators (Shamkina, 2023). The most common ways in which artificial intelligence is applied in education are listed below (Figure 5):

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1. Personalization of learning experiences. AI-based platforms can collect and analyze student data on interaction with educational materials, exercise completion time, test results, and overall performance to understand the attitudes and needs of each student. From this data, artificial intelligence tools can design personalized training routes and adapt them in real time to the student's progress.

2. Learning outcomes. Educational solutions based on ML (Machine Learning) are capable of processing previously collected data on students' academic performance, attitudes and social conditions and categorizing them into different archetypes.

3. Algorithms can then compare and identify relationships between these categories of students and their typical school outcomes. Automation of redundant faculty tasks like evaluating tests with the right grading software, saving time to interact with students. These tools (Figure 5) are already perfectly capable of correcting multiple choice and true-false exercises, but thanks to advances in natural language processing (NLP), they will become increasingly effective at time to also check written short answers and essays.

4. Administrative workflows. AI can be used to automate many administrative activities, including student application processing, enrollment, facilities management, human resources procedures, recruiting, etc. The UK Department of Education decided to adopt a system that can autonomously process digital correspondence and therefore speed up its tracking rate of incoming emails.

5. Support for students with special needs. AI-enabled assistants can provide them with personalized learning paths and exercises to ensure they get the best education possible. For example, AI text-to-speech software can read educational content aloud and give visually impaired students the same opportunity to learn.

6. Resource planning in addition to improving learning outcomes. AI can also optimize the management of school infrastructure. For example, AI can perform time-consuming and error-prone resource planning tasks, such as calculating the correct amount of learning materials.

7. Curricular design. AI can process enormous amounts of data about students' progress, interests, competencies, and challenges in a given year and advise curriculum designers on how to improve the effectiveness of their large-scale teaching programs. AI algorithms can identify patterns and trends, evaluate the effectiveness of certain pedagogical approaches, and predict the results of different educational strategies.

8. Continuous assistance during learning. The combination of ML-powered adaptive learning and natural language processing makes virtual assistants extremely flexible and, consequently, a valuable ally for students. Helping them experiment and be less anxious about making mistakes, something many students tend to do in the classroom in front of their teachers and peers.

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b. Institutional Strategies Cases in universities ●

University of Florida – AI Across the Curriculum

In 2020, the university began offering an introductory course that teaches basic AI knowledge and concepts to all students. Since then, universities and departments have tailored AI courses to their specific needs and disciplines. Students can begin their AI learning with the exclusive course, “AI Fundamentals”, which requires no prior knowledge of AI, engineering, or computer science. Students can continue with more advanced AI courses in various disciplines at the institution. This model of teaching AI across disciplines is the foundation for building an AI university and results in a larger, more diverse group of students who will graduate from the institution with AI knowledge.

The goal in developing a highly innovative approach to such a program – campus-wide – is to provide an opportunity for every undergraduate student to engage and learn about AI, both within their discipline and, more importantly, in an interdisciplinary manner that is often more reflective of the real-world work environment.

Expected results: 1) New course pedagogy to facilitate student learning about complex topics in AI; 2) Students entering the workforce with greater ability to exploit the synergy and productivity of diverse team environments, and 3) Developing processes to better link student learning with the skills and knowledge of the industry/employer in the picture fast-paced technology and artificial intelligence.

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University of California, Berkeley – AI Policy Hub

AI Policy Hub is an interdisciplinary initiative that trains forward-thinking researchers to develop effective policy and governance frameworks to guide artificial intelligence, today and in the future. Research conducted through the AI Policy Hub helps policymakers and other AI decision makers act with foresight in rapidly changing social and technological environments.

Its mission is to cultivate an interdisciplinary research community to anticipate and address policy opportunities for safe and beneficial AI. Their vision is a future where AI technologies do not exacerbate division, harm, violence, and inequality, but instead foster human connection and social well-being.

They also collaborate with other UC Berkeley departments and centers that contribute work on AI governance and policy.

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Gonzaga University – AI in Higher Education

Since fall 2022, the Development Office has been collaborating with various campus faculty to explore the dangers and potential of AI in higher education. They have generated scenarios based on games and case studies, used various tools, and taken advantage of the wide range of information that AI can extract from. “As we continue to explore and harness the power of technology in the higher education space, it is becoming increasingly evident that AI has immense potential to transform the way courses are designed and developed.”

This shift toward AI-powered course design and development has the potential to create a more personalized and effective learning experience for students. Its strategy rests on five essential points: ●

●

●

●

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Leverage AI for personalized learning experiences. Modify course content and structure to better suit individual learning styles. If a student is struggling with a particular topic, the AI system can provide additional resources or alternative explanations to help them understand better.

Using AI to improve course content. With AI-powered content analysis tools, faculty can assess the relevance and quality of their course content, ensuring it is up-to-date and aligned with desired learning outcomes.

Promoting active learning AI tools can be designed to pose thought-provoking questions, encourage discussions, and stimulate critical thinking, thereby making learning more interactive and engaging.

Evaluation and feedback AI-powered tools can automate the grading process, providing immediate feedback to students and freeing up time for faculty to focus on other vital aspects of teaching.

Improve accessibility With AI-powered transcription and translation services, course materials can be accessible to a broader audience, breaking down language barriers and fostering inclusivity.

The incorporation of AI in higher education, especially in course design and development, represents a seismic shift in the way teaching and learning is approached. As universities adapt their programs to incorporate AI tools, a more personalized, effective, and inclusive learning environment can be created.

Ultimately the focus remains on the goal: improving student learning outcomes and equipping them with the knowledge and skills they need to excel in the real world.

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