Here are many of the frequently asked questions – and answers! – about the computing-related degree options at Illinois.
How many CS and Data Science undergraduate degree programs exist? What is the difference between them? 
The University of Illinois offers 20+ pathways to incorporate computing and data science into undergraduate education and research:
Computer Science (CS) degree from The Grainger College of Engineering, with a rigorous curriculum of computer science and other technical requirements
Mathematics & CS and Statistics & CS, which includes a technical core curriculum in mathematics or statistics blended with a technical computer science core curriculum
14 CS + X* degrees, which include a technical computer science core curriculum blended with the core curriculum of select other majors
7 X* + Data Sciences degrees, which include a data science core curriculum (taught by Computer Science, Mathematics, Statistics, and the iSchool) blended with the core curriculum of other select majors. Note: X + Data Science students are advised by the college/department admitting them.
CS minor, which includes a computer science core curriculum that can be paired with nearly any other major on campus
* “X” represents the major blended with either the core computer science curriculum or the core data science curriculum. Several colleges across campus offer majors – you apply to and graduate from the college that provides the X major. 
With so many options, how do you choose which major to apply for?
The CS major in The Grainger College of Engineering is for students primarily focused on computer science. CS is a very rigorous and technical major. It is also a highly competitive program with an admit rate of 6.7%.
The blended CS majors and X + Data Science majors are for students passionate about or interested in the partner program. Students in these majors understand that computing technologies influence nearly every field, including the other areas blended with CS or Data Science. They want to build computational tools with the potential to innovate, change, and shape the future of that field. As a result, Illinois graduates with a Mathematics & CS, Statistics & CS, CS + X, or X + Data Science degree are uniquely positioned to launch their careers or pursue graduate studies in various fields.
For more information, please see the Illinois Admissions blog post: Get to Know Computer Science (And Majors Similar to Computer Science!)
What’s the difference between Computer Science (CS) and Computer Engineering (CE) majors?
At The Grainger College of Engineering, Computer Engineering resides in a different department, the Department of Electrical and Computer Engineering. Generally speaking, Computer Engineering focuses more on designing and developing the physical components used in computing (hardware). In contrast, Computer Science focuses more on computation to solve problems (software). 
Note: CE majors are not eligible for the CS minor.
How does the coursework differ between majors in CS, CS + X, and X + Data Science?
At The Grainger College of Engineering, the CS Engineering and CS + X majors share a standard computer science core curriculum. The X + Data Science degree has a core curriculum that includes courses taught by the Departments of Computer Science, Mathematics, Statistics, and the iSchool. The Grainger College of Engineering CS majors must also take 6-8 technical electives from the 400-level courses in specialized areas of Computer Science. Students majoring in the other blended CS programs or the X + Data Science majors must take a substantial concentration of coursework in another department, thus blending CS or Data Science with another discipline. 
The college that houses each major may also have varying general education requirements, especially language other than English (LOTE) and science. Therefore, we recommend prospective students consult each program of interest’s specific degree requirements and planning forms linked from each program page. 
Note: only students enrolled in Computer Science at The Grainger College of Engineering are eligible to pursue the Fifth Year Master’s program offered by The Grainger College of Engineering.
Do the Math & CS, Stats & CS, CS + X, or X + Data Science programs result in dual degrees?
No, they do not. Each is a single major that leads to a Bachelor of Science degree.
What computer languages are taught in required CS courses at Illinois?
Core courses for CS majors are taught in Java (Kotlin), C++, C programming, Python, and assembly languages. Some elective courses and registered student organizations (RSOs) may teach additional programming languages.
What kinds of employment opportunities do CS programs prepare you for?
Experience tells us that students in all computer science programs at the University of Illinois are well-prepared for software design and development positions in almost any field. The 400-level CS electives required for students in CS Engineering add technical breadth and depth to that knowledge. The blended CS majors will be well-qualified for more specialized jobs that require applying computational solutions to problems in the fields joined with their CS degrees. At Illinois, this includes blended degrees in Accountancy, Advertising, Animal Sciences, Anthropology, Astronomy, Chemistry, Crop Sciences, Data Science, Economics, Education, Finance, Geography & Geographic Information Science, Linguistics, Mathematics, Music, Philosophy, and Statistics.
More specific information about career options is available on the Undergraduate Admissions Majors pages.
Visit the Illini Success site to learn about Illinois graduates’ career aspirations and achievements, including employment and starting salary statistics.
What career fields are typical for Math & CS, Stats & CS, and CS + X majors? 
Mathematics & CS: specialized fields of scientific computation, financial engineering, software engineering, and theoretical computer science.
Statistics & CS: focus on data analysis, data visualization, and data mining and prepares students for business, computer, and medical fields. 
CS + Advertising: specialized fields of computational advertising, data analytics, mobile advertising, and application design and development. This degree program prepares students for graduate study in CS and Advertising fields.
CS + Animal Sciences: areas of animal sciences with a technology, data handling, and management focus and a genomics focus, such as precision animal science, bioinformatics, computational biology, and web programming for animal-related companies. This degree program prepares students for advanced study at the graduate level. 
CS + Anthropology: specializations in biological anthropology, linguistic anthropology, sociocultural anthropology, computational anthropology, and archeology. This degree program also prepares graduates for CS-related work in social media and online communities. 
CS + Astronomy: focuses on astronomically motivated computational challenges and working with large data sets through data analysis, visualization, mining, design and modeling, astrophysical and numerical simulations, and image processing. The requirements of this degree alone are not adequate preparation for graduate study in Astronomy. Students will need to work with an astronomy advisor for additional coursework recommendations. 
CS + Chemistry: career fields related to imaging technologies, drug design, quantum chemical calculations, molecular dynamics simulations, computations & molecular modeling, molecular therapeutics, and visualization. These specializations may include analysis of experimental imaging data to visualization of in vivo chemical reactions. 
CS + Crop Sciences: career fields related to crop genetics, agricultural IT, bioinformatics, web programming for agricultural companies, computational biology, data analysis, and precision agriculture. In precision agriculture, graduates can specialize their skills to focus on remote sensors, embedded systems, and satellite imagery. In addition, students can incorporate the degree into the five-year Crop Science BS/MS (non-thesis) degree. 
CS + Economics: specialized fields include econometrics, business, financial economics & consulting, industrial organization, and mathematical economics. This degree program prepares students for graduate work in Computer Science, Economics, Statistics, Financial Engineering, and Policy.
CS + Education: has two tracts to choose from – Learning Sciences or Secondary Education. With these degree programs, students can create more effective and equitable educational environments or provide more equitable access to computer science education. In-demand fields include, but are not limited to, algorithm development, online platform design, educational game design and simulations, and development of accessible and assistive technologies. These degrees will prepare students for graduate-level advanced study and immediate entry into the workforce at educational institutions, research centers, non-profits, and technology companies. 
CS + Geography & Geographic Information Systems (GGIS): specializations include programmers, analysts, and researchers in roles varying from developing geographic information software and analytic techniques to solving spatial problems related to healthcare, transportation, national security, environmental degradation, and natural hazards. Graduates from this degree may also specialize in cartography, computational geography, and geospatial technology.
CS + Linguistics: technical fields related to the computer-natural language relationship, including speech analysis and synthesis, translation, the storage and retrieval of large amounts of data, computational linguistics, artificial intelligence, software design, and user interface design
CS + Music: technical specializations within the music, audio, and digital media industries, including audio processing and computer music. In addition to art-related jobs, graduates with a deep understanding of audio and computation are well-prepared for the specialized fields of speech recognition, audio/speech communication, and audio compression.
CS + Philosophy: computer science specializations focus on ethics, logic, and privacy, especially in fields of artificial intelligence and security in a digital age.
How competitive are admissions to the CS major in The Grainger College of Engineering? 
It is highly competitive. For several years, the CS major in The Grainger College of Engineering has broken the record for the most first-year applications received by any program in the university’s history. The high school GPA and standardized test profiles are typically on the high end of those listed for The Grainger College of Engineering. Of course, good grades and high test scores alone do not guarantee admission to one of our programs. Potential applicants should carefully read the holistic review process and major-specific criteria that admissions officers look for from applicants. The current admit rate to CS in engineering is 6.7%.
While we welcome your interest in and application to CS in engineering, we strongly encourage prospective students to explore Illinois’s computing-related degree options. A great place to start is the Illinois Admissions blog post, Get to Know Computer Science (And Majors Similar to Computer Science!)
Note: CS in engineering is not available as a second-choice major and is not open for transfers once students are admitted to Illinois.
Is being admitted to the Math & CS, Stats & CS, CS + X, or X + Data Science easier than the CS major in engineering?
All the undergraduate programs with “Computer Science” or “Data Science” in the title are very competitive. The high school GPA and standardized test profiles of students in these majors are very similar. The current admit rate to CS + X majors is 25.4%.
We strongly encourage prospective students to explore Illinois’s computing-related degree options. A great place to start is the Illinois Admissions blog post, Get to Know Computer Science (And Majors Similar to Computer Science!)
Note: The blended CS majors are not available as second-choice majors. Majors in the Gies College of Business and The Grainger College of Engineering are closed for transfers once students are admitted to Illinois. However, many other majors, including some of the blended CS majors, are open for transfer. Students should consult their academic advisor to explore the options since each department and college has varying requirements.
If I’m admitted to another department at Illinois, can I transfer into a computing-related major?
However, many other majors, including some blended CS majors, are available for transfer once students are admitted. We also encourage students to consider adding the CS minor to their current major. Students should consult their academic advisor to explore the options since each department and college has varying requirements.
If I’m in another department on campus, can I apply for a second bachelor’s degree in a CS program?
For any seats we can provide in our programs, we prioritize students seeking their first and only bachelor’s degree. Unfortunately, that means we take very few second-degree candidates – students who are exceptional and provide a well-considered, compelling justification for needing an entire second major in a CS program. 
We would also encourage students to consider adding the CS minor to their current major. Students should consult their academic advisor to explore the options since each department and college has varying requirements.
What are the benefits of the CS minor, and how do I apply?
Nearly all Illinois undergraduate degrees can be paired with a Computer Science minor.
The CS minor can be a great way to add computer science to the area of study you’re most passionate about—from agriculture to the arts, from media to the sciences, or from business to engineering, to name a few. That said, declaring a CS minor does not provide registration advantages in CS courses. We cannot guarantee that a student can obtain all the courses needed to complete the minor (completing a minor is not a graduation requirement). Students should begin the minor no later than the first semester of their Junior year since the program takes a minimum of 4 semesters to complete.
Note: CS, CS + X, and CE majors are not eligible for the CS minor.
If you have questions about undergraduate CS and blended degree programs that this FAQ page has not addressed, please email  undergrad@cs.illinois.edu, and an academic advisor will respond. 
Thomas M. Siebel Center for Computer Science
201 N. Goodwin Avenue, MC-258
Urbana, IL 61801
Phone:
Fax:
Email:
The Grainger College of Engineering


Chicago Office
200 South Wacker Drive, 7th Floor
Chicago, IL 60606

source

PERSPECTIVE article
Front. Educ., 04 December 2024
Sec. Digital Education
Volume 9 – 2024 | https://doi.org/10.3389/feduc.2024.1499495
This article is part of the Research TopicGenerative AI Tools in Education and its Governance: Problems and SolutionsView all 10 articles
The emergence of generative AI in education introduces both opportunities and challenges, especially in student assessment. This paper explores the transformative influence of generative AI on assessment practices, drawing from recent training workshops conducted with educators in the Global South. It examines how AI can enrich traditional assessment approaches by fostering critical thinking, creativity, and collaboration. The paper introduces innovative frameworks, such as AI-resistant assessments and the Process-Product Assessment Approach, which emphasize evaluating not only the final product but also the student’s interaction with AI tools throughout their learning journey. Additionally, it provides practical strategies for integrating AI into assessments, underscoring the ethical use and preservation of academic integrity. Addressing the complexities of AI adoption, including concerns around academic misconduct, this paper equips educators with tools to navigate the intricacies of human-AI collaboration in learning settings. Finally, it discusses the significance of institutional policies for guiding the ethical use of AI and offers recommendations for faculty development to align with the evolving educational landscape.
Generative AI (Gen AI) has undergone significant advancements, transforming from simple text generation tools into highly sophisticated systems capable of producing human-like content across a broad spectrum of domains (Feuerriegel et al., 2024). With the emergence of advanced models like GPT-40 and the GPT-01 preview model, AI is now able to perform a range of complex tasks, including text analysis and natural language understanding, and it even demonstrates creativity in writing and problem solving (Shahriar et al., 2024). This leap in AI capabilities has opened up exciting new possibilities for its integration into education, particularly in the areas of providing feedback and enhancing assessment processes.
In the past year, the rapid development and increasing power of generative AI tools have sparked a revolution in education. These tools have enabled educators to devise innovative teaching strategies and redesign student tasks to better meet the demands of the AI era (Khlaif et al., 2023). As a result, there is a growing movement among decision makers, educators, practitioners, and researchers aimed at exploring how generative AI can be integrated into both higher and public education systems, creating opportunities for significant advancements across a variety of educational contexts (Noroozi et al., 2024).
Within education, generative AI is proving to be much more than a tool for administrative efficiency; it is opening new doors for improving student engagement and transforming the way learning is assessed. Furthermore, generative AI is becoming a point of competition among higher education institutions that strive to meet global goals such as the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 4 (quality education) and SDG 5 (gender equality) (George and Wooden, 2023; Pisica et al., 2023).
However, despite these promising advancements, significant challenges remain in the process of integrating AI into higher education. Based on the author’s experience, supported by numerous studies, these challenges are not limited to financial costs. Rather, a critical issue lies in the attitudes and perspectives of the academic community, which are often accompanied by a lack of clear vision and strategy within higher education institutions, particularly in many countries of the Global South (Bozkurt et al., 2023; Jin et al., 2024). Even though UNESCO has published guidelines (Holmes and Miao, 2023) to assist decision makers in these regions, progress in AI adoption remains slow. While individual initiatives, such as professional development workshops, attempt to address these gaps and integrate AI into educational practices, many countries continue to lag behind despite these efforts.
Thus, while generative AI offers tremendous potential for reshaping education and advancing institutional goals, its full integration will require overcoming not only technical and financial barriers but also addressing the broader academic and structural challenges that persist, especially in less-resourced regions.
Assessment has always been a cornerstone of the educational process. It serves as a critical mechanism to measure learning outcomes, provide feedback, and ensure that educational goals are met (Botuzova et al., 2023). The development of traditional assessments, from standardized tests and exams to authentic assessments and the emerging new flipped-assessment approach, continues in light of technological advancements and emerging teaching strategies (Aziz et al., 2020). Such developments aim to align with 21st-century skills and enhance the quality of education, thereby meeting one of the sustainability goals.
While the use of AI in education has been explored extensively in areas such as personalized learning, instructional design, and administrative automation, there exists a significant gap in understanding how generative AI can be integrated into assessment practices, particularly in higher education. Most current research focuses on the application of AI to automate grading or provide instant feedback, but there is limited research on how AI can transform the nature of assessments themselves. Additionally, much of the discourse around AI in education has been concentrated in the Global North, with insufficient attention paid to how these technologies can be adapted and implemented in Global South contexts, where resource constraints and educational challenges differ. This paper addresses these gaps by exploring how generative AI can be harnessed to rethink assessment methods, focusing on early adopters in the Global South and their diverse academic fields, such as medical education, humanities, and engineering.
The purpose of this paper is to provide a comprehensive framework for rethinking assessment in the era of generative AI, drawing on both theoretical insights and practical experiences from workshops conducted with faculty members in higher education. By examining how generative AI can be integrated into assessment strategies, this paper aims to offer educators, particularly in the Global South, practical solutions for designing more meaningful, AI-enhanced assessments that align with modern educational needs. Furthermore, the present paper will highlight how these assessments can promote the development of higher-order thinking skills, encourage creativity, and provide more equitable and scalable solutions to challenges faced by educators worldwide. Through this exploration, this paper seeks to contribute to the ongoing dialog about the future of assessment in an AI-driven society.
During the last year, the author designed and delivered a series of comprehensive training workshops and public lectures aimed at rethinking educational assessment in the age of artificial intelligence (AI). These sessions were conducted both in-person and online, bringing together 333 educators from various countries. The workshops were offered in both Arabic and English to accommodate the diverse linguistic backgrounds of the attendees. These sessions focused on equipping educators with theoretical insights and practical tools to redesign student assignments, fostering critical thinking, creativity, and ethical AI use in the classroom. Table 1 presents the demographic information of the participants in the training workshops.
Table 1. Demographic information of the participants in the training workshops.
The workshops were structured into two key components: theoretical discussions and practical applications. In the theoretical part, the author explored cutting-edge models and frameworks for assessing student work in the context of AI. One of the key models introduced was the AI Task Assessment Scale, a tool designed to help educators systematically evaluate student performance in assignments that incorporate AI tools. Another major framework discussed was the Process-Product Assessment Model, an emerging approach that not only assesses the final product of student work but also evaluates the quality of the steps students take during the process. This includes how they develop AI prompts and collaborate with AI tools throughout their learning journeys. This shift in assessment philosophy places greater emphasis on human-AI interaction, which offers educators a richer and more comprehensive understanding of student learning and critical-thinking processes.
In the practical component, educators engaged in hands-on activities to design AI-resistant assignments. This approach aligns with the AI-Resistance Assessment Scale (AIAS), which the author developed to guide educators in creating tasks that promote critical engagement and ethical AI use. Educators explored various ways to encourage students to use generative AI tools constructively, particularly during the brainstorming phase of assignments. This method of integrating AI into the learning process ensures that students remain responsible for the content generated by AI, fostering accountability and deepening their understanding of the material. By encouraging students to develop AI prompts carefully and strategically, educators can assess the cognitive processes involved in human-AI collaboration rather than just focusing on the output.
A central theme of the workshops was the Process-Product Approach, which was discussed extensively as a new and emerging method of assessment. Educators were trained to use this approach to evaluate both the quality of the final product and the process that led to it. This includes assessing the interactivity between students and AI, as well as the steps students take in developing effective prompts. The aim was to help educators design tasks that allow for a detailed evaluation of human-AI collaboration, ensuring that students engage critically with AI tools rather than use them passively. This focus on the process helps educators identify the depth of student understanding and decision making during their assignments.
As the educators engaged in these discussions, they reflected on ways to incorporate AI into student work while fostering critical thinking. One key suggestion was to allow students to use generative AI during the brainstorming and proofreading stages of their assignments. However, to ensure that students critically engage with the AI-generated content, educators recommended requiring students to write a reflection paper about their experiences using AI in course projects. This reflection would not only detail the students’ interactions with AI but also include a critique of the ideas and content generated by the tools. The purpose of this approach is to equip students with critical-thinking skills and foster the development of essential 21st-century skills such as problem solving, ethical decision making, and the ability to assess the credibility of AI-generated information.
Educators were particularly interested in designing a variety of AI-resistant tasks, which refer to assignments structured to minimize the risk of over-reliance on AI-generated content. These tasks require students to engage deeply with the subject matter, ensuring that they contribute their own insights and creativity while using AI as a supportive tool rather than a replacement for original thought. Emphasis was placed on designing assignments that challenge students to use AI ethically, critically, and responsibly. By requiring students to validate and reflect on AI outputs, educators can assess not only the final product but also how effectively students navigate the complexities of human-AI collaboration.
During the workshops, educators raised concerns about the implications of generative AI on academic integrity. Many feared that AI might dilute the originality of student work or lead to plagiarism. To address these concerns, we discussed strategies for shifting the focus of assessment from the final product to the learning process itself. By assessing the quality of the interactions between students and AI, educators can maintain academic integrity while still embracing the potential benefits of AI. Tasks that require problem solving, critical thinking, and creativity were highlighted as particularly resistant to AI overreach, as they demand original input from students and cannot be fully automated.
One of the key insights that emerged from these discussions was the need for authentic assessment, meaning assignments that reflect real-world challenges and require students to apply their knowledge in complex, context-rich situations. These tasks make it more difficult for students to rely entirely on AI-generated content, as they require critical thinking, collaboration, and ethical reasoning. This shift toward authentic and process-based assessments allows educators to better evaluate student learning in a way that safeguards academic integrity while fostering deeper engagement with the course material.
Looking ahead, the author plans to publish a detailed guide for teachers and academics on how to design AI-resistant assessments using the AI-Resistance Assessment Scale (AIAS). This guide will provide educators with practical strategies for creating tasks that enhance critical thinking, creativity, and ethical AI use. In the meantime, educators can begin applying the concepts discussed in the workshops by using the AI Task Assessment Scale to navigate the integration of AI into their teaching while maintaining the integrity of student learning.
In conclusion, these training workshops offered more than just theoretical insights; they provided a platform for educators from diverse backgrounds to collaboratively rethink the future of educational assessment in the age of AI. By focusing on critical thinking, human-AI collaboration, and ethical responsibility, educators are now better equipped to design assessments that are not only resistant to the misuse of AI but also promote deeper, more meaningful student learning. These workshops laid the foundation for educators to harness the potential of AI while safeguarding the essential values of creativity, originality, and academic integrity in education.
After each training session, educators engaged in open discussions where they exchanged best practices for integrating AI into teaching and professional development. These conversations allowed faculty members to reflect on their individual experiences and learn from their colleagues’ diverse approaches. The collaborative environment fostered an exchange of practical resources, including AI-based activities and assignments. By sharing these resources, educators were able to explore the broad potential of generative AI tools and consider how to implement them effectively within their individual teaching contexts. This peer-to-peer learning was crucial in expanding educators’ understanding of AI’s applications in education.
One of the most critical points of discussion during the workshops was the need to redesign student assessments to align with the realities of the AI era. Faculty explored strategies for adapting assessments that not only integrate AI tools but also ensure students are challenged in ways that AI cannot easily replicate. For instance, they emphasized context-specific solutions tailored to their disciplines and the unique challenges of their institutions. These discussions underscored the importance of designing assessments that encourage deeper engagement with the subject matter, creativity, and the development of critical-thinking skills. The focus was on moving beyond rote learning and creating tasks that would require students to demonstrate higher-order thinking, problem-solving abilities, and ethical decision making in their interactions with AI tools.
A recurring theme throughout the workshops was the necessity of establishing a clear institutional vision and policy regarding the integration of AI into both teaching and research. Educators agreed that institutions must provide clear guidance on how AI should be used, with policies that encompass both the educational and research contexts. As noted by Pedro et al. (2019), such policies are essential for fostering positive attitudes toward AI and encouraging educators to adopt these tools in thoughtful and productive ways.
Moreover, institutions with well-defined AI policies can play a pivotal role in raising awareness about AI literacy and competencies. Faculty and students alike need a structured framework to navigate the ethical and practical implications of using AI in their academic work. As Spivakovsky et al. (2023) highlighted, these policies can help build a more informed academic community which is capable of leveraging AI’s benefits while being mindful of its challenges.
Another key takeaway from the workshops was the importance of developing policies specifically for AI-enhanced courses, with particular focus given to addressing assessment-related challenges. Such policies can provide students with clear guidelines on when and how to use AI tools in their coursework. By setting clear boundaries, these policies enhance students’ understanding of the ethical implications of AI use, ensuring that the technology is applied responsibly.
Faculty members also discussed the importance of balancing AI’s benefits with the need to preserve the authenticity of student work. A well-crafted policy framework would empower educators to manage AI’s use effectively, reducing concerns about cheating and misuse. For example, a few educators reported creating GPT models for their courses, uploading lecture content, and using the tool to generate both closed and open-ended questions. While some colleagues appreciated this practice, others criticized it, noting that AI-generated questions were sometimes repetitive or misaligned with course material. These conversations highlighted the need for a thoughtful, well-monitored approach to integrating AI in assessments.
A recurring theme in the workshops was the importance of viewing AI as a collaborative tool that supports, rather than replaces, student learning. Some educators shared their experience using generative AI to help design assignments or generate initial ideas for tasks. These assignments were structured around the AI-Resistance Assessment Scale (Petihakis et al., 2024), which encourages students to engage critically with AI tools for brainstorming, idea generation, proofreading, and project development.
Educators suggested linking this AI assessment scale to Bloom’s Taxonomy (Forehand, 2010) to ensure a deeper alignment with educational objectives. This integration encourages students to reflect on the ideas generated by AI, compare them with their own thoughts, and improve their critical-thinking skills. By combining AI-supported tasks with a clear course policy, students are guided to use AI responsibly while deepening their understanding of the subject matter.
During the workshop discussions, the facilitator introduced the concept of Product-Process Assessment as an emerging evaluation approach that shifts the focus from the final product to the process involved in completing a task. This approach is particularly relevant in the context of human-AI collaboration, where the interaction between the learner and AI tools becomes a vital component of the assessment.
The Product-Process Assessment method evaluates how students develop prompts to guide AI tools and how they collaborate with AI throughout the learning journey. This model encourages educators to assess not only the final output but also the decisions students make during the process. Faculty members were encouraged to involve students in the development of evaluation rubrics, creating a partnership in the assessment process. This collaborative approach allows students to critically engage with AI-generated content, refining their initial ideas based on comparison and critical reflection.
Another significant topic covered during the workshops was the role of AI in fostering authentic assessments which focus on real-world tasks and applications. Authentic assessments differ from traditional tests by requiring students to apply their knowledge in practical, context-rich scenarios which reflect the complexities they will encounter in professional settings (Fatima et al., 2024; Thanh et al., 2023). Xia et al. (2024) emphasized the importance of authentic assessment in the AI era, where tools like ChatGPT can challenge students’ beliefs and promote critical thinking. In this context, students demonstrate their understanding by applying their knowledge to evaluate complex cases generated by ChatGPT. Moving beyond traditional knowledge-based assessments, there is a growing need to focus on problem solving, data interpretation, and case-study-based questions. This highlights the significance of carefully designing new assessment strategies that prioritize the learning process, cultivate higher-order thinking, and immerse students in meaningful, real-world tasks.
For example, instead of a traditional essay, educators might ask students to create podcasts, develop multimedia presentations, or solve real-world problems using AI tools. These assessments foster creativity, collaboration, and critical thinking, promoting higher-order skills that are essential in an AI-driven world.
An educator in the Faculty of Humanities and Educational Sciences developed the following authentic assessment for her students:
“Develop a comprehensive marketing campaign for a new product. Please integrate AI level 2 for brainstorming and generating ideas, then critique the generated ideas by AI tool.”
In the business field, an educator provided the following example:
“Develop a sales strategy for a new generative AI tool designed to assist academic writing in a student’s area of interest.”
Another educator in medical education, specifically nursing, provided the following:
Create a comprehensive nursing care plan for a patient with a specific set of health issues (e.g., a diabetic patient with hypertension and a risk of stroke).
AI-resistant assessments have emerged as a critical approach in the AI era, particularly as educators grapple with concerns about academic integrity (Khlaif et al., 2024b). The rise of generative AI has made it easier for students to produce automated content, increasing the risk of plagiarism and over-reliance on AI-generated outputs. To counter these challenges, educators and researchers have introduced AI-resistant assessments designed to minimize the risk of students misusing AI tools while still leveraging AI for productive learning.
The central idea behind AI-resistant assessments is to create tasks that cannot be solved easily by AI, encouraging deeper engagement from students. This type of assessment typically requires higher-order thinking skills such as critical analysis, creativity, and problem solving—areas in which AI struggles to fully replicate human abilities. Additionally, AI-resistant assessments focus on the process of learning rather than just the final product. This means that students are evaluated based on their interactions with AI tools, including how they develop prompts, refine their ideas, and critically engage with AI-generated content.
The purpose of reflective writing is to assess learners’ ability to connect theoretical skills and knowledge with individual experiences, fostering critical thinking and self-awareness (Sudirman et al., 2021). In reflection writing, students are encouraged to use AI tools to brainstorm ideas, generate drafts, or receive feedback on their work. However, rather than submitting AI-generated content directly, students are required to write a reflection on how they used AI and detail the specific prompts they used, their rationale for selecting certain AI-generated content, and how they modified or critiqued the AI’s suggestions. Therefore, reflective writing serves as an excellent AI-resistant form of assessment because it involves personal, subjective experiences that are challenging for AI to replicate. Since reflective writing requires self-reflection and connections to individual experiences, the authenticity and originality of the student’s thoughts are essential (Zeng et al., 2024). Reflection writing is considered an AI-resistant assessment for the following justifications, as reported by the majority of the educators in the training sessions:
• Personalization: Reflective assignments will encourage students to share their unique experiences and interpretations, which reduces the risk of AI-generated responses.
• Critical thinking: They foster critical thinking and analysis skills that go beyond factual recall or synthesis, which AI might generate easily.
• Process documentation: Students are often asked to document their thought processes and revisions, making it difficult for AI to substitute original, evolving reflections.
• Ethical considerations: In fields like medicine, reflective practice is tied to professional development and empathy, involving inherently human qualities.
For further clarification, a faculty member from medical education presented an example of reflected writing in her course and the rubric (Table 2) she used to evaluate her students’ final work.
Table 2. The rubric used to assess the medical reflective assignment from the faculty member.
This reflective component forces students to engage critically with AI, ensuring they remain responsible for the final product. All of these are based on a rubric developed by the educators. She reported that: “In my field of teaching, medical education, I use reflective writing to assess the students’ responses to real-life clinical experiences.”
Here is the example relating to medical education:
Assignment title: Reflective essay on handling ethical dilemmas in clinical practice
• Instructions: Write a 1,500-word reflective essay about an ethical dilemma you encountered or observed during your clinical practice. Describe the situation, the ethical conflict, and the resolution, if any. Reflect on your emotions, the challenges faced, and how this experience has influenced your professional growth.
• Objective: Encourage students to connect their theoretical knowledge of medical ethics with real-world experiences and reflect on their personal development.
• Expected outcome: Students will demonstrate their understanding of ethical principles (e.g., autonomy, beneficence) and their application in clinical settings.
The findings from the training workshops connect directly with UNESCO’s AI guidelines for education and research (Holmes and Miao, 2023), as well as align with recent academic research on the transformative potential of AI in educational settings (Moorhouse et al., 2023; Xia et al., 2024). This alignment is evident in several key areas: ethical AI use, inclusivity, the enhancement of educational quality, teacher development, and institutional policy frameworks.
The emphasis on AI-resistant assessments and the ethical use of AI in education mirrors UNESCO’s advocacy for human-centric AI integration that ensures fairness, transparency, and academic integrity (Holmes and Miao, 2023; Moorhouse et al., 2023). The workshops’ focus on fostering critical thinking and ethical considerations in AI usage, particularly in assessment, supports recent findings that stress the need for ethical deployment of AI to prevent academic dishonesty and to protect student data (Xia et al., 2024; Saaida, 2023). Moreover, AI-driven assessment tools must address issues of fairness and data privacy, which are critical to maintaining trust in AI-enhanced educational environments (Lyanda et al., 2024).
The introduction of the Process-Product Assessment Model in the workshops aligns with the literature advocating for the evaluation of both final outcomes and the process of human-AI interaction in learning (Xia et al., 2024). This approach is supported by studies emphasizing that AI-powered assessments should evaluate not only the end product but also critical thinking, problem-solving, and the quality of decision-making in human-AI collaborations (Lyanda et al., 2024). This shift from product-based to process-based assessment encourages deeper student engagement with AI and fosters self-regulated learning, as highlighted by adaptive learning platforms that dynamically adjust to student performance (Selwyn, 2022; Gamage et al., 2023).
The workshops, which involved educators from the Global South, resonate with UNESCO’s goal to bridge the digital divide and promote inclusivity in education. As AI technologies become more pervasive, equitable access to these tools is critical, particularly in underserved regions (Holmes and Miao, 2023; Saaida, 2023). By focusing on how AI tools can be leveraged to enhance learning experiences in diverse educational contexts, the workshops contribute to SDG 4’s goal of ensuring inclusive and equitable quality education for all (Lyanda et al., 2024). AI-driven personalized learning tools, such as intelligent tutoring systems and adaptive assessments, offer tailored learning pathways that address diverse learning needs, further enhancing educational inclusivity (George, 2023).
A key aspect of the workshops was the emphasis on building teacher capacity to integrate AI responsibly into their assessments. This echoes UNESCO’s call for professional development initiatives that equip educators with the skills to navigate AI-enhanced teaching and learning environments (Holmes and Miao, 2023). As recent studies have demonstrated, AI tools like chatbots and virtual assistants can play a pivotal role in supporting teachers, reducing their workload, and enhancing their ability to provide timely and personalized feedback (Lyanda et al., 2024; Saaida, 2023). Teacher training programs, as recommended by George (2023), should prioritize critical thinking, creativity, and adaptability, preparing educators for the changing dynamics of AI-driven education (George, 2023).
The workshops also addressed the need for clear institutional policies that guide the ethical integration of AI in education, a point echoed in UNESCO’s guidelines and supported by recent studies (Holmes and Miao, 2023; Moorhouse et al., 2023; Khlaif et al., 2024b). As educational institutions increasingly adopt AI technologies, it is crucial to develop localized policies that ensure academic integrity, protect data privacy, and promote inclusivity (Saaida, 2023; Lyanda et al., 2024). In addition, the need for ongoing dialog between educators and policymakers to ensure that AI implementation aligns with broader educational goals has been highlighted in the literature (Selwyn, 2024).
Participants in the workshops explored innovative assessment redesigns that integrate AI tools like ChatGPT, not just for brainstorming but for fostering critical thinking and ethical reasoning. This reflects findings by Wang et al. (2024) that students recognize the value of AI-generated content but emphasize the importance of critically engaging with AI outputs. The workshops went beyond this by providing educators with strategies to design assessments that hold students accountable for AI-generated content (Khlaif et al., 2024a), ensuring they reflect on and critique the material they engage with, thus promoting ethical AI use in student assessments (Lyanda et al., 2024).
Concerns about academic integrity, raised in both the workshops and current research, underscore the importance of developing AI-resistant assessments that encourage authentic student engagement and reflection. Studies by Xia et al. (2024) and Khlaif et al. (2024b) highlight the challenges of detecting AI-generated content, emphasizing the need for assessments that foster student responsibility and ethical decision-making. The workshops’ focus on reflective writing tasks, where students critically engage with AI outputs, aligns with calls in the academic community for more authentic and process-based assessments (Lyanda et al., 2024).
This paper offers several important theoretical implications for the field of education, particularly in the context of integrating AI into assessment practices. Traditionally, assessment methods in higher education have relied on standardized tests, essays, and projects. However, the introduction of AI shifts the focus toward more dynamic and complex forms of assessment, such as AI-resistant assessments and human-AI collaboration. The paper explores how AI can not only automate grading but also transform the way educators assess higher-order thinking skills like critical analysis, creativity, and ethical reasoning. This challenges existing assessment paradigms by pushing the boundaries of what can be evaluated in educational settings.
One of the key theoretical contributions of the paper is the proposal of the AI Task Assessment Scale and the Process-Product Assessment Model, both of which offer frameworks for assessing student interactions with AI. These models provide a foundation for rethinking how students’ learning processes, rather than just their final outcomes, are evaluated. The paper also highlights the need for a shift from purely product-based assessments to assessments that evaluate both the process and the student’s engagement with AI tools, which is a relatively unexplored area in educational theory. Additionally, the discussion on AI-resistant assessments introduces a new layer of complexity in how educators design tasks that mitigate the risk of over-reliance on AI, thus enriching the theoretical dialog on the integrity of assessment.
From a practical perspective, the present paper provides educators and institutions with actionable strategies to integrate AI into their teaching and assessment practices. For faculty members, it outlines how to design assessments that encourage student engagement with AI while maintaining academic integrity. For example, the paper discusses the implementation of the AI-Resistance Assessment Scale (AIAS), which can be used to design tasks that promote critical thinking and responsible AI use. It also offers practical guidance on how educators can assess not only the final output but also the process students undergo when using AI tools, thus providing a more comprehensive understanding of student learning.
In addition to classroom practices, this paper underscores the importance of institutional policies in supporting the integration of AI into education. It recommends that institutions develop clear policies that guide both students and educators on how AI should be used ethically in educational settings. The.
se policies can help mitigate risks such as academic dishonesty while ensuring that AI is used as a tool to enhance, rather than replace, learning. Furthermore, the paper provides a roadmap for professional development, emphasizing the need for continuous training for educators to stay up to date with AI advancements and their implications for assessments.
This research has demonstrated the potential of integrating generative AI into educational assessments while addressing the ethical, pedagogical, and institutional challenges that accompany its use. The development of AI-resistant assessments, such as the Process-Product Assessment Model, offers a practical framework for evaluating both the final output and the collaborative steps between students and AI tools. These findings emphasize the importance of fostering critical thinking, creativity, and ethical reasoning, ensuring that AI serves as a supplement rather than a replacement in learning processes.
Moreover, the research highlights the necessity of institutional policies that guide AI integration in education, particularly in regions with limited resources, such as the Global South. By offering solutions that address academic integrity and promote responsible AI use, this study bridges gaps in the global educational landscape and provides actionable strategies for educators to implement AI-enhanced assessments effectively.
Overall, the findings reinforce the idea that AI can be a transformative force in education when used thoughtfully, with an emphasis on fostering lifelong learning skills, preserving ethical standards, and adapting assessment methods to meet the evolving demands of an AI-driven world. There still exists a need for a complete assessment plan that fits the different subjects in higher education. Future research should work on creating clear rules to make these assessments the same for everyone. This will help ensure fairness across different fields of study and solve the challenges that come with using advanced AI tools like ChatGPT in education.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
WA: Conceptualization, Formal analysis, Investigation, Writing – review & editing, Funding acquisition. ZK: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing.
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
We used ChatGPT 4o for proofreading and the authors took responsibility for the accuracy of the generated text.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Aziz, M. N. A., Yusoff, N. M., and Yaakob, M. F. M. (2020). Challenges in using authentic assessment in 21st century ESL classrooms. Int. J. Eval. Res. Educ. 9, 759–768. doi: 10.11591/ijere.v9i3.20546
Crossref Full Text | Google Scholar
Botuzova, Y., Iievliev, O., Okipniak, I., Yandola, K., and Charkina, T. (2023). Innovative approaches to assessment in pedagogical practice: new technologies, methods and development of objective assessment tools. Cadernos de Educação Tecnologia e Sociedade 16, 386–398. doi: 10.14571/brajets.v16.n2.386-398
Crossref Full Text | Google Scholar
Bozkurt, A., Junhong, X., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., et al. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): a collective reflection from the educational landscape. Asian J. Dist. Educ. 18, 53–130.
Google Scholar
Fatima, S. S., Sheikh, N. A., and Osama, A. (2024). Authentic assessment in medical education: exploring AI integration and student-as-partners collaboration. Postgrad. Med. J. 783. doi: 10.1093/postmj/qgae088
Crossref Full Text | Google Scholar
Feuerriegel, S., Hartmann, J., Janiesch, C., and Zschech, P. (2024). Generative ai. Bus. Inf. Syst. Eng. 66, 111–126. doi: 10.1007/s12599-023-00834-7
Crossref Full Text | Google Scholar
Forehand, M. (2010). Bloom’s taxonomy. Emerg. Perspect. Learn. Teach. Technol. 41, 47–56.
Google Scholar
Gamage, K. A., Dehideniya, S. C., Xu, Z., and Tang, X. (2023). ChatGPT and higher education assessments: more opportunities than concerns?. Journal of Applied Learning and Teaching 6.
Google Scholar
George, A. S. (2023). Preparing students for an AI-driven world: rethinking curriculum and pedagogy in the age of artificial intelligence. Partners Univ. Innov. Res. Publ. 1, 112–136.
Google Scholar
George, B., and Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Admin. Sci. 13:196. doi: 10.3390/admsci13090196
Crossref Full Text | Google Scholar
Holmes, W., and Miao, F. (2023). Guidance for generative AI in education and research. Paris, France: UNESCO Publishing.
Google Scholar
Jin, Y., Yan, L., Echeverria, V., Gašević, D., and Martinez-Maldonado, R. (2024). Generative AI in higher education: a global perspective of institutional adoption policies and guidelines. arXiv preprint arXiv:2405.11800.
Google Scholar
Khlaif, Z. N., Ayyoub, A., Hamamra, B., Bensalem, E., Mitwally, M. A., Ayyoub, A., et al. (2024b). University teachers’ views on the adoption and integration of generative AI tools for student assessment in higher education. Educ. Sci. 14:1090. doi: 10.3390/educsci14101090
Crossref Full Text | Google Scholar
Khlaif, Z. N., Mousa, A., Hattab, M. K., Itmazi, J., Hassan, A. A., Sanmugam, M., et al. (2023). The potential and concerns of using AI in scientific research: ChatGPT performance evaluation. JMIR Med. Educ. 9:e47049. doi: 10.2196/47049
PubMed Abstract | Crossref Full Text | Google Scholar
Khlaif, Z., Odeh, A., and Bsharat, T. R. (2024a). “Generative AI-powered adaptive assessment” in Power of persuasive educational Technologies in Enhancing Learning (USA: IGI Global), 157–176.
Google Scholar
Lyanda, J. N., Owidi, S. O., and Simiyu, A. M. (2024). Rethinking higher education teaching and assessment in-line with AI innovations: a systematic review and Meta-analysis. Afr. J. Emp. Res. 5, 325–335. doi: 10.51867/ajernet.5.3.30
Crossref Full Text | Google Scholar
Moorhouse, B. L., Yeo, M. A., and Wan, Y. (2023). Generative AI tools and assessment: guidelines of the world’s top-ranking universities. Comp. Educ. Open 5:100151. doi: 10.1016/j.caeo.2023.100151
Crossref Full Text | Google Scholar
Noroozi, O., Soleimani, S., Farrokhnia, M., and Banihashem, S. K. (2024). Generative AI in Education: Pedagogical, Theoretical, and Methodological Perspectives. International Journal of Technology in Education 7, 373–385.
Google Scholar
Pedro, F., Subosa, M., Rivas, A., and Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
Google Scholar
Petihakis, G., Farao, A., Bountakas, P., Sabazioti, A., Polley, J., and Xenakis, C. (2024). AIAS: AI-ASsisted cybersecurity platform to defend against adversarial AI attacks. In Proceedings of the 19th International Conference on Availability, Reliability and Security (pp. 1–7
Google Scholar
Pisica, A. I., Edu, T., Zaharia, R. M., and Zaharia, R. (2023). Implementing artificial intelligence in higher education: pros and cons from the perspectives of academics. Societies 13:118. doi: 10.3390/soc13050118
Crossref Full Text | Google Scholar
Saaida, M. B. (2023). AI-driven transformations in higher education: opportunities and challenges. Int. J. Educ. Res. Stud. 5, 29–36.
Google Scholar
Selwyn, N. (2022). The future of AI and education: Some cautionary notes. European Journal of Education, 57, 620–631.
Google Scholar
Selwyn, N. (2024). On the limits of artificial intelligence (AI) in education. Nordisk tidsskrift for pedagogikk og kritikk, 10, 3–14.
Google Scholar
Shahriar, S., Lund, B. D., Mannuru, N. R., Arshad, M. A., Hayawi, K., Bevara, R. V. K., et al. (2024). Putting GPT-4o to the sword: a comprehensive evaluation of language, vision, speech, and multimodal proficiency. Appl. Sci. 14:7782. doi: 10.3390/app14177782
Crossref Full Text | Google Scholar
Spivakovsky, O. V., Omelchuk, S. A., Kobets, V. V., Valko, N. V., and Malchykova, D. S. (2023). Institutional policies on artificial intelligence in university learning, teaching and research. Inform. Technol. Learn. Tools 97, 181–202. doi: 10.33407/itlt.v97i5.5395
Crossref Full Text | Google Scholar
Sudirman, A., Gemilang, A. V., and Kristanto, T. M. A. (2021). Harnessing the power of reflective journal writing in global contexts: a systematic literature review. Int. J. Learn. Teach. Educ. Res. 20, 174–194. doi: 10.26803/ijlter.20.12.11
Crossref Full Text | Google Scholar
Thanh, B. N., Vo, D. T. H., Nhat, M. N., Pham, T. T. T., Trung, H. T., and Xuan, S. H. (2023). Race with the machines: assessing the capability of generative AI in solving authentic assessments. Australas. J. Educ. Technol. 39, 59–81. doi: 10.14742/ajet.8902
Crossref Full Text | Google Scholar
Wang, Y., Wang, L., and Siau, K. L. (2024). Human-Centered Interaction in Virtual Worlds: A New Era of Generative Artificial Intelligence and Metaverse. International Journal of Human–Computer Interaction 1–43.
Google Scholar
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., and Chiu, T. K. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. Int. J. Educ. Technol. High. Educ. 21:40. doi: 10.1186/s41239-024-00468-z
Crossref Full Text | Google Scholar
Zeng, W., Goh, Y. X., Ponnamperuma, G., Liaw, S. Y., Lim, C. C., Jayarani, D., et al. (2024). Promotion of self-regulated learning through internalization of critical thinking, assessment and reflection to empower learning (iCARE): a quasi-experimental study. Nurse Educ. Today 142:106339. doi: 10.1016/j.nedt.2024.106339
Crossref Full Text | Google Scholar
Keywords: assessment, Gen AI, human-AI engagement, UNESCO, AI-resisted assessment, quality of education, sustainability
Citation: Awadallah Alkouk W and Khlaif ZN (2024) AI-resistant assessments in higher education: practical insights from faculty training workshops. Front. Educ. 9:1499495. doi: 10.3389/feduc.2024.1499495
Received: 20 September 2024; Accepted: 11 November 2024;
Published: 04 December 2024.
Edited by:
Reviewed by:
Copyright © 2024 Awadallah Alkouk and Khlaif. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zuheir N. Khlaif, emtobGFpZkBuYWphaC5lZHU=
^†ORCID: Wejdan Awadallah Alkouk, https://orcid.org/0009-0006-8803-2147
Zuheir N. Khlaif, https://orcid.org/0000-0002-7354-7512
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Share on
Share on
Research integrity at Frontiers
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Supplementary Material

source

One of the most critical components in machine learning projects is the database management system. With the help of this system, a large number of data can be sorted and one can gain meaningful insights from them. According to the Stack Overflow Survey report 2019, Redis is the most loved database, whereas MongoDB is the most wanted database.
In this article, we list down 10 top databases used in machine learning projects.
(The list is in alphabetical order)
Apache Cassandra is an open-source and highly scalable NoSQL database management system that is designed to manage massive amounts of data in a faster manner. This popular database is being used by GitHub, Netflix, Instagram, Reddit, among others. Cassandra has Hadoop integration, with MapReduce support. 
Advantages:
Couchbase Server is an open-source, distributed, NoSQL document-oriented engagement database. It exposes a fast key-value store with managed cache for sub-millisecond data operations, purpose-built indexers for fast queries and a powerful query engine for executing SQL-like queries. 
Advantages:
Amazon DynamoDb a fully managed, multi-region, durable database with built-in security, backup and restore, and in-memory caching for internet-scale applications. This accessible database has been using by Lyft, Airbnb, Toyota, Samsung, among others. DynamoDB offers encryption at rest which eliminates the operational burden and complexity involved in protecting sensitive data. 
Advantages: 
Elasticsearch is built on Apache Lucene and is a distributed, open-source search and analytics engine for all types of data including textual, numerical, geospatial, structured and unstructured data. Elasticsearch is the central component of the Elastic Stack which is a set of open-source tools for data ingestion, enrichment, storage, analysis, and visualisation.
Advantages:
The Machine Learning Database (MLDB) is an open-source system for solving big data machine learning problems, from data collection and storage through analysis and the training of machine learning models to the deployment of real-time prediction endpoints. In MLDB, machine learning models are applied using Functions, which are parameterised by the output of training Procedures, which run over Datasets containing training data.
Advantages:
Written in C and C++, Microsoft SQL Server is a relational database management system (RDBMS). This database helps in gaining insights from all the data by querying across relational, non-relational, structured as well as unstructured data. 
Advantages: 
Written in C and C++, MySQL is one of the most popular open-source relational database management systems (RDBMS) powered by Oracle. It has been used by successful organisations such as Facebook, Twitter, YouTube, among others.
Advantages:
MongoDB is a general-purpose, document-based, distributed database which is built for advanced application developers. Since this is a document database, it mainly stores data in JSON-like documents. It provides support for aggregations and other modern use-cases such as geo-based search, graph search, and text search.
Advantages: 
PostgreSQL is a powerful, open-source object-relational database system which uses and extends the SQL language combined with many features that safely store and scale the most complicated data workloads. This database management system aims to help developers build applications, administrators to protect data integrity, build fault-tolerant environments and much more. 
Advantages: 
Redis is an open-source, in-memory data structure store which is used as a database, cache and message broker. It supports data structures such as strings, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, etc. The database has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence. 
Advantages:
Want to advertise in AIM? Book here
The core curriculum of their diploma programmes has been thoughtfully redesigned to align with the AI transformation happening across the industry.
Join top GCC leaders in Goa from 20 to 22 June 2025 to shape the future of digital transformation
Email:
info@aimmediahouse.com
Our Offices
AIM India
1st Floor, Sakti Statesman, Marathahalli – Sarjapur Outer Ring Rd, Green Glen Layout, Bellandur, Bengaluru, Karnataka 560103
AIM Americas
99 South Almaden Blvd. Suite 600 San Jose California 95113 USA
© Analytics India Magazine Pvt Ltd & AIM Media House LLC 2025
EtherealX’s first launch is slated for March 2027.

source

Our Privacy Statement & Cookie Policy
By continuing to browse our site you agree to our use of cookies, revised Privacy Policy and Terms of Use. You can change your cookie settings through your browser.
Wang Yan
Copied
The 2025 World Digital Education Conference, themed “Education Development and Transformation: The Era of Intelligence,” opens in Wuhan, central China’s Hubei Province, May 14, 2025. /CFP
Editor’s note: Wang Yan, a special commentator on current affairs for CGTN, is a senior specialist from Beijing Foreign Studies University. The article reflects the author’s opinion, and not necessarily the views of CGTN.
China is committed to advancing digital transformation of education and scaling up intelligent technologies in education to create an inclusive, equitable, intelligent quality education system that can facilitate lifelong learning for people of all ages. This will be achieved, in particular, through high-level international cooperation, technology-empowered education transformation, infrastructure connectivity and resource sharing, as well as collaboration to strengthen ethical and security safeguards in digital education.
This was the message delivered by Chinese Vice Premier Ding Xuexiang at the recent 2025 World Digital Education Conference (WDEC) held in Wuhan, central China’s Hubei Province from May 14 to 16, bringing together heads of international organizations, representatives from universities and schools, and experts to discuss education development and transformation in the era of intelligence.
Why digital education? Because studies show that 40 percent of the core skills that are the norm today will be replaced by artificial intelligence (AI) by 2030 or even sooner, given the pace of technological advancement. The majority of students are already using AI, which is as high as 92 percent in the universities in the UK.
Against this backdrop, the focus of education has shifted from simple knowledge transfer to 21st-century competencies that increasingly value critical thinking and high-order thinking skills. Pedagogy is also changing, from lectures to human-machine collaboration that maximizes the quality and efficiency of teaching.
It is in such context that the 2025 WDEC was convened to explore new approaches to innovate education content, methods and modalities with the transformative power of emerging technologies, through sharing policies and practices and envisioning the future directions of education development.
The conference showed the future of education, demonstrating future classrooms, future schools and the future work of teachers as well as the scenario of collaborative teacher-student-machine interaction that will become a norm in the AI era. The participants also made school, university, vocational college and open university visits to observe how digital technology can empower teaching and learning processes in different learning stages.  
Overall, it was a cross-culture, cross-disciplinary and cross-sector dialogue in which stakeholders from various countries shared their insights and perspectives on the approaches, strategies and methods of the digital transformation of education.
A highlight was the ministerial dialogue between China and the Association of Southeast Asian Nations focusing on talent cultivation, academia-industry cooperation and pedagogical application of AI, showing the power of cooperation. Documents on digital education launched at the conference, such as a white paper on smart education in China and the Global Digital Education Development Index 2025, outlined visions for transforming the education system while showcasing the effect of synergy created through collaboration of key stakeholders.
Humanoid robots for pedagogical application are exhibited at the 2025 World Digital Education Conference in Wuhan, central China’s Hubei Province, May 14, 2025. /CFP
AI is central to the discourse of digital education, bringing the most fundamental changes to education, and presenting both opportunities and challenges. While the essence of education remains unchanged, AI offers opportunities for scalable personalized education on one hand.
On the other hand, it is also widening the digital divide across the globe. By the end of 2020, nearly two-thirds of the world’s school-age children had no Internet access at home. Validating AI tools for education is not an easy thing. Besides the challenges of formulating AI ethics regulations, there is also the concern about super-intelligence surpassing human intelligence.  
In the face of these changes, educators around the world are grappling with the same question: What will be the future trends of AI development? What changes will it bring to the transformation of education? What will be the core competencies for teachers, leaders and students in the AI era? What global frameworks, common principles can direct education transformation?
Although AI is a key instrument for shaping the future of education, it is human collaboration that inspires and catalyzes solutions to the shared challenges. Such collaboration will continue. This is what the annual WDEC is about with its collective efforts to advance global education reform and development.
Moreover, the World Digital Education Alliance, established last year, held its first general assembly at this year’s conference, providing a platform for further dialogue, networking and development of knowledge products.
All this is part of the journey integrating AI and education for a more inclusive and human-centered system. Equipped with increased knowledge and global digital cooperation, we are together embracing a better future of education.
(If you want to contribute and have specific expertise, please contact us at opinions@cgtn.com. Follow @thouse_opinions on Twitter to discover the latest commentaries in the CGTN Opinion Section.)
EXPLORE MORE
CHOOSE YOUR LANGUAGE
DOWNLOAD OUR APP
互联网新闻信息许可证10120180008
Disinformation report hotline: 010-85061466
EXPLORE MORE
CHOOSE YOUR LANGUAGE
DOWNLOAD OUR APP
互联网新闻信息许可证10120180008
Disinformation report hotline: 010-85061466

source