Ai-Based Automated Grading and Feedback Systems: Technologies, Challenges and Future Directions in Higher Education

Scientific research on the assessment of educational outcomes using artificial intelligence technologies in higher education is still fragmented, especially in the context of Ibero-American countries and the transforming educational systems, which include Kazakhstan. Despite the active introduction of digital platforms, analytical tools, and automated assessment systems, comprehensive research revealing innovative approaches, methodological foundations, and ethical aspects of AI-based assessment remains limited. In this regard, this mini-review is aimed at filling the identified research gap by analyzing key theoretical works and modern empirical studies that determine the prospects for further development of this field. The methodological basis of the review is a conceptual model that considers assessment using artificial intelligence as a multi-level system that includes automated analysis of educational data, personalized feedback, predictive evaluation mechanisms, and algorithmic support for pedagogical decisions. Within the framework of this model, assessment is interpreted not as an isolated control tool, but as an integrated component of the educational environment that influences individual learning trajectories, academic success and student engagement. It should be noted that this mini-review is one of the first studies in which this conceptual framework is applied to a comparative analysis of assessment practices using artificial intelligence in the Ibero-American and Kazakh contexts of higher education. The review included 12 peer-reviewed scientific publications published since 2020, which addressed the issues of automated assessment, learning analytics, adaptive feedback systems and ethical aspects of the introduction of artificial intelligence in the university environment of these regions. The paper analyzes the main innovations in the field of AI-based assessment, including the use of behavioral and cognitive analytics, automated formative assessment, forecasting academic risks and personalized assessment support for students. Special attention is paid to contextual factors such as the level of digital readiness of students, the institutional features of universities, as well as differences in assessment practices between public and private educational organizations. Along with technological innovations, the mini-review examines the key ethical challenges associated with the use of artificial intelligence in assessing educational achievements. Such challenges include algorithmic bias, limited transparency of assessment decisions, issues of protecting students’ personal data, and the risks of increasing educational inequality. The analysis shows that the manifestation of these ethical issues varies in Ibero-American countries and Kazakhstan, reflecting differences in educational policies, the level of digital infrastructure, and socio-economic conditions. Based on research using a variety of methodological approaches, including quantitative, qualitative, and mixed designs, this mini-review demonstrates the multiplicity of interpretations and practices of applying artificial intelligence in higher education assessment. At the same time, the need to expand interdisciplinary research aimed at analyzing poorly studied categories of students and alternative educational trajectories in the digital environment is emphasized. In conclusion, the educational and ethical implications of further implementation of AI-based assessment in higher education are considered. Given the increasing role of automated assessment systems and the desire of universities to improve the objectivity and fairness of assessment, the development of methodologically sound and ethically responsible models for the use of artificial intelligence is of particular relevance. At the same time, taking into account the regional characteristics of educational systems makes the comparative analysis of the Ibero-American and Kazakh experience an important area of modern research in the field of digital education.

Problem (Introduction). The rapid integration of generative artificial intelligence (AI) into education has created unprecedented opportunities for personalised learning, yet it has also raised serious concerns about the reliability of AI-generated content. Large language models (LLMs) optimise for plausibility rather than truth, and they can fabricate facts, citations or even legal cases (When AI Gets It Wrong: Addressing AI Hallucinations and Bias - MIT Sloan Teaching & Learning Technologies, n.d.). Such hallucinations threaten academic integrity: students may unknowingly absorb falsehoods, while teachers could inadvertently reproduce inaccuracies in course materials. Empirical studies reveal that AI systems can hallucinate from less than 1 % up to 15–40 % of cases in educational tasks depending on the model and domain (Figure 1), and systematic reviews note that over‑reliance on AI dialogue systems is linked to diminished critical thinking, increased technology dependence and the spread of misinformation (Zhai et al., 2024). Purpose. This article aims to analyse the scope and causes of AI-generated misinformation in education and to develop evidence-based recommendations for mitigating these risks. It combines technical insights on model architecture and training data with pedagogical strategies to foster AI literacy. The goal is to ensure that AI enhances rather than undermines learning. Methods. A systematic literature review of over 80 sources, including scientific articles, policy documents (AI Act, UNESCO guidelines), and empirical studies, provided a theoretical foundation. Comparative analysis of hallucination rates across models (Makhno et al., 2025; Lelièvre et al., 2025) informed the quantitative assessment. The study also modelled mitigation strategies such as Retrieval‑Augmented Generation (RAG) and Chain‑of‑Verification and evaluated pedagogical interventions like lateral reading and AI literacy programmes. Results. The findings show that hallucinations stem from both internal (model architecture and context limitations) and external (biased or incomplete training data) factors. Even top models misinform 1–3% of the time, whereas widely used free systems can err 15–40% of the time when generating bibliographies or research proposals (Balch & Blanck, 2024). Hallucinations manifest in various forms: logical errors, mathematical mistakes, fabricated sources and factual inaccuracies. Their educational consequences include decreased critical thinking, increased plagiarism (“AI‑giarism”) and a risk of spreading disinformation. Regulatory frameworks classify educational AI systems as high‑risk; Annex III of the EU AI Act lists educational AI systems for admissions, assessment and monitoring as high‑risk and sets obligations for accuracy, transparency and human oversight (Nguyen, 2025). Among mitigation strategies, RAG reduces hallucinations, while Chain‑of‑Verification and self‑consistency improve reliability. Pedagogically, teaching students lateral reading, updating academic policies, and redesigning assessments to require reflection and verification are essential. Originality. Unlike purely technical surveys or broad commentaries, this study bridges AI research with educational practice and policy, providing a holistic perspective tailored to Ukrainian higher education. It synthesises international findings with local realities, offers a taxonomy of AI errors, presents original visualisations (Table 1 and Figure 1), and proposes a multi-level framework combining technical, pedagogical and regulatory solutions. The article emphasises that AI hallucinations are not simply technical bugs but systemic challenges requiring cultural change. Conclusion. To harness AI’s benefits in education, stakeholders must recognise and mitigate the problem of misinformation. Improving models (via RAG, verification chains), enhancing AI literacy, and adhering to high-risk regulatory standards will help ensure that AI supports, rather than sabotages, learning. Future research should focus on domain-specific hallucination rates, real-time fact-checkers for Ukrainian-language content, and longitudinal studies on AI’s cognitive impact. Ultimately, balancing technological innovation with human oversight and ethical principles will determine whether AI becomes a trustworthy educational ally or a source of confusion.

This research aims to evaluate the possible positive aspects of Artificial Intelligence (AI) technologies in English language teaching that occur when training learners of English as a Foreign Language (EFL) or English as a Second Language (ESL). The study seeks to understand how AI technology and methodologies enhance language learning and teaching, including the unique possibilities that arise when considering learners and educators with an evolving role in increasingly larger education systems and educational environments. It seeks to understand the potential of AI for personalizing English language training. Also, by asking the question study, if there are methods including Natural Language Processing (NLP), Machine Learning (ML), and AI-based analytics or tools, can they personalize learning experiences for EFL-ESL learners? A qualitative study was completed through interviews with learners from different experience levels engaged in EFL or ESL programs that used AI-based tools in the pedagogy. The results demonstrate that AI has positive opportunities for personalized language education in the EFL and ESL contexts, the ability to enhance language skills in specific domains, and offer customized language learning experiences. This research has indicated challenges that surround cultural elements, technical challenges, and distinctly human teaching and learning elements. The research also offers helpful information for English language learning and recognizes the shifting nature of personal learning experience whether enhanced through AI, the practical requirement of incorporating AI will also provide a unique lens and perspective in English language learning and teaching, recognize the affordances AI can offer, alongside students and language learning never being exclusively cultural or human.

Academic Writing Assessment (AWA) in higher education is essential for students, educators, and institutional stakeholders, as it not only evaluates students’ writing proficiency but also informs pedagogical practices and supports ongoing curriculum development. Despite an expanding body of literature in this domain, comprehensive bibliometric studies that systematically map the publication landscape, identify influential contributors, and trace the evolution of research themes remain scarce. This study addresses this gap by conducting a systematic bibliometric analysis of AWA research in higher education, examining publication trends, thematic development, and key authorship from 2000 to 2025. A total of 545 Scopus-indexed journal articles were analyzed using VOSviewer, Biblioshiny-R, Microsoft Excel, and OpenRefine. The results indicate a consistent growth in publications, particularly after 2010, with a marked surge since 2018. Teng M.F was identified as the most prolific author, offering future researchers clear reference points for seminal works in the field. The most cited publication, Strobl with 212 citations, examined the integration of digital technologies in academic writing. Geographically, the United Kingdom accounted for the highest publication output. Thematic analysis revealed central topics including "academic writing," "higher education," and "assessment," alongside emerging themes such as "collaborative learning," "ChatGPT," "artificial intelligence," and "academic integrity." Current trends suggest that technological integration is reshaping AWA, frequently prioritizing efficiency and measurable outcomes over students’ metacognitive, literacy, critical thinking, and analytical development. These findings highlight the need for more holistic assessment approaches that consider both the processes and outcomes of academic writing. Future research should integrate evaluations of writing processes, learning outcomes, and technological applications to foster a comprehensive understanding of AWA in higher education.

Artificial intelligence (AI) is changing assessment in higher education in terms of automatic grading, adaptive testing, intelligent feedback, and distance proctoring. The paper considers the perceptions of AI-based assessment tools that hold popularity among the stakeholders of higher education (students, instructors, and administrators) in the context of perceived usefulness, fairness, reliability, ethical issues (privacy and bias), and impacts on academic integrity and learning outcomes. The study will gather quantitative survey data (N = 420) and qualitative interview data (n = 24) in a diverse sample of universities that have piloted or implemented AI assessment tools in 2020-2025 using a mixed-methods research design. Validated perceived usefulness, trust in automation, perceived fairness, privacy, and self-reported academic behavior are quantitative measures. Independent-samples t-tests to compare the attitudes of the students and instructors, multiple regression to predict the acceptance of the AI assessment based on the perceived usefulness and ethical issues, and ANOVA to determine variations across the disciplines and previous exposure to the AI tools are all tests of hypothesis. The thematic examination of human attitudes to pedagogy, academic integrity, transparency, and professional development requirements can be discussed as qualitative. The results show a conditional approval of the use of AI-based assessment: stakeholders appreciate efficiency and customized feedback, but they have constant reservations about the bias of algorithms, obscure grading, security threats to privacy through data transcriptions and remote proctoring, and the possible loss of human-driven assessment habits. The results of regression indicate that perceived usefulness ( =.42, p =.001), and perceived fairness ( =.29, p =.002) have positive prediction on acceptance, and privacy concern has negative prediction on acceptance ( = -.31, p =.001). T-tests are more persuasive of pragmatic acceptance among students than among instructors (t(418) = 3.12, p =.002). ANOVA shows that there is a difference in disciplinary difference: STEM participants indicated that they placed more trust in automated grading of objective tasks compared to humanities participants (F(3,416) = 6.21, p < .001). Qualitative topics talked of the necessity of human review, articulation of scoring reasons, strong data management, and employee education. The study has theoretical value, that is, it incorporates a technology acceptance and socio-technical fairness perspective and presents more practical recommendations: design transparent algorithms, embrace layered integrity, train educators, and engage stakeholders in assessment redesign. The authors conclude that AI-based testing can complement higher education assessment activities provided that the implementation is supported with safeguards against fairness, transparency, and pedagogic fit.

Feedback is an integral part of education and there is a substantial body of trials exploring and confirming its effect on learning. This evidence base comes mostly from studies of compulsory school age children; there is very little evidence to support effective feedback practice at higher education, beyond the frameworks and strategies advocated by those claiming expertise in the area. This systematic review aims to address this gap. We review causal evidence from trials of feedback and formative assessment in higher education. Although the evidence base is currently limited, our results suggest that low stakes‐quizzing is a particularly powerful approach and that there are benefits for forms of peer and tutor feedback, although these depend on implementation factors. There was mixed evidence for praise, grading and technology‐based feedback. We organise our findings into several evidence‐grounded categories and discuss the next steps for the field and evidence‐informed feedback practice in universities. Context and implications Rationale for this study To gain a better understanding of effective formative assessment and feedback approaches in higher education (HE). To promote a more evidence‐informed approach to teaching and learning in universities. Why the new findings matter The findings highlight a small number of promising strategies for formative assessment and feedback in HE. They also draw attention to a lack of (quality) evidence in this area overall. Implications for policy‐makers and practitioners Universities and their regulators/funders should be encouraging and supporting more, high‐quality research in this important area. Researchers in the field also need to look to developing more ambitious, higher‐quality studies which are likely to provide robust, causal conclusions about academic effectiveness (or other outcomes). Those involved in teaching and learning in university should use the findings to inform evidence‐informed approaches to formative assessment and feedback and to challenge approaches which do not appear to have foundations in strong evidence. Students could be made more aware of teaching and learning approaches that are likely to support their academic progress.

The phenomenon of Performance-Based Research Assessment (PBRA) in higher education is a national, regional, and global one. It is a broad-ranging area of research and commentary and not a field that is easy to capture or summarize. In attempting to do so, this article will predominantly draw upon the wide range of research that has been conducted on national systems of PBRA in higher education as well as the comparisons made of these different national systems. It will also address, however, the continued significance of “global league tables,” most notably the Academic Ranking of World Universities produced by Shanghai Jiao Tong University in China and the Times Higher Education Supplement University World Rankings in the United Kingdom. It is important to note that these PBRA systems are constantly in flux, so anything that is written can quickly become outdated. This article is organized in terms of themes, which capture the historical and political significance of predominantly national PBRA systems and the significance they have in challenging our ideas of universities, how they should be governed and funded and ultimately in potentially re-shaping the mission of higher education institutions and changing the work done by academics and the production of disciplinary knowledge. It is a diverse research field with a wide range of disciplinary perspectives that have been taken to investigate this topic and the span of disciplinary theories and methodologies, including disciplines such as statistics, policy analysis, and sociology. Those authors writing on PBRA in higher education are academics across all disciplines within the broad areas of the sciences, social sciences, and humanities as well as non-academic managers and leaders within higher education. It is an issue that is debated by individuals across the whole system of higher education. The literature can be broadly based within two areas, a perspective that views PBRA systems as a means to improve accountability and efficiency of research in higher education, which attempts to compare and contrast the PBRA systems and argue for the best and most efficient way of organizing them while recognizing that improvements must be made to ensure fairness and equity. The other is a critique of the whole project of PBRA systems, which are seen to be allied to neo-liberal and new public management ideological agendas and employs critical theoretical perspectives to show the deleterious impacts of such systems on knowledge production within universities and academic work and identity.

ChatGPT (Generative Pretrained Transformer) is currently the most sophisticated chatbot. It can create impressive prose in seconds, unlike other chatbots, and it has generated a lot of hype and doomsday predictions when it comes to student assessment in higher education and a variety of other topics (Rudolph et al., 2023). Nonetheless, despite its impressive capabilities, various reports on ChatGPT have consistently revealed significant remaining challenges (Bang et al., 2023). This study aims to explain the advantages and disadvantages of ChatGPT. A qualitative approach was conducted. In-depth interviews were used with ten key informants, employing purposive sampling. Content analysis and NVivo were utilised to analyse the data. The findings revealed that ChatGPT is a natural language processing (NLP) tool that has the potential to revolutionise the way we communicate. This artificial intelligence (AI) technology can generate text, allowing users to easily create personalised content, and it has gained widespread popularity. However, the reaction has been mixed, with praise for its benefits and potential applications offset by criticism of its limitations and potential drawbacks. Furthermore, ChatGPT is an extremely effective tool. However, it cannot replace human thought and, if not properly fine-tuned, it has the potential to produce biased or insulting content. Thus, it is critical to bear ethical considerations in mind when implementing this technology.

The purpose of this paper is to determine the directions and to specify the infrastructural role of higher education in the social and investment model of economic growth in view of the specifics and perspectives of emerging economies. Correlation analysis is used to determine the dependence between the directions of higher education’s development and the target results of its contribution to the implementation of the social and investment model of economic growth in emerging economies. These connections are specified with the help of regression analysis. Then, the simplex method is used to determine target landmarks for the selected directions. According to the experience of the top emerging economies with the highest level of economic innovative development in 2020, maximization (up to 100%) of knowledge-intensive employment (+299.29%), medium-tech and hi-tech manufacturing (+223.42%) and e-participation (+19.98%) requires the increase in the number of students per 1 lecturer in higher education by 387.26%, growth of mobility in higher education by 1,116.27%, and increase in QS university ranking by 139.13%. It is proved that only three directions of higher education’s development are significant and have to be implemented to support social and investment model of economic growth in emerging economies: increase in the number of students per 1 lecturer in higher education, growth of mobility in higher education, and increase in QS university ranking.

Artificial intelligence (AI)-based learning tools are increasingly integrated in higher education, offering benefits such as personalized learning experiences, real-time feedback, and increased flexibility. However, effective design and implementation strategies for these tools are not well established. This study addresses this gap through a systematic literature review with two main objectives: (1) to summarize the design features of AI-based learning tools currently employed in higher education, focusing on aspects such as algorithm types, training datasets, modes of information presentation, and their roles in the learning process; and (2) to assess their impacts on college students’ cognitive, skill-based, and affective learning outcomes. Our review encompasses 63 peer-reviewed articles published between January 2014 and April 2024. Notably, approximately half of the reviewed studies employ publicly available AI systems for instructional purposes (n = 32), while the other half develop proprietary AI-based learning tools (n = 31). 26 studies use AI techniques to generate and deliver multimodal learning materials. Moreover, we identify three primary roles of AI in higher education: assessment and evaluation (n = 45), personalized feedback and recommendations (n = 46), and intelligent tutoring (n = 26). In terms of learning outcomes, although AI tools generally improve cognitive knowledge acquisition and affective outcomes, their effectiveness in developing cognitive process and skills varies significantly. Lastly, we provide recommendations for optimizing the design and implementation of AI-based learning tools in higher education and outline promising directions for future research in this field.

The progress of sustainability within higher education has steadily increased in focus over the last decade and has increasingly become a topic of academic research. As institutions investigate, implement and market sustainability efforts, there is a myriad of sustainability assessment methodologies currently available. This assortment of standards being used by institutions do not help students and faculty assess the level of sustainability uniformly between institutions. A universal framework was proposed for facilitate stakeholder’s review of comparing sustainability assessments in higher education. This research reviews the creation of the framework and results from testing in an online environment. The lack of data collected during the testing phase provides some anecdotal evidence regarding what stakeholder consider important in terms of sustainability within higher education and may also indicate that there is no need for a universal sustainability assessment in higher education to be used directly by stakeholder.

Reviewed by: Outcomes Assessment in Higher Education: Views and Perspectives Cia Verschelden Outcomes Assessment in Higher Education: Views and Perspectives, ed. Peter Hernon and Robert E. Dugan. Westport, Connecticut: Libraries Unlimited, 2004. 350 p. $50(ISBN: 1-59158-098-6) In the fall of 2000, the Higher Learning Commission visited our campus on a re-accreditation site visit. The following spring we received their report—we were reaccredited but they would be back for a "focused visit" in 2005 to look at our progress on assessment of student learning. That summer, I took on the task of "capacity building" in an effort to develop a positive and sustainable assessment of student learning culture at a large, public land-grant institution. I have since met with dozens of people to talk about student learning outcomes, assessment criteria and measures, and using assessment results to improve student learning. I found it almost impossible to get through this book! In virtually every chapter, I found myself taking time to highlight interesting or essential parts, make notes in the margins, or notes to myself about what I need to do with this or that section. My notes were comments like: "The Provost must see this ASAP"; "This is exactly the argument that I've been having with Professor __________ in biology for two years"; "Every department head needs this list and this chart tomorrow;" "I told them that's how it's supposed to happen!" This book addresses real issues. It reflects on precisely the kinds of challenges that I and my colleagues from across the country experience every day in our work. I found that many of the articles provided a barometer of sorts for me. I found myself assessing our progress and reflecting on the "opportunities" still ahead. The experts in this volume have described the path that promises to lead eventually to an effective "culture of assessment" at our institutions. In several of the articles, the authors provide progress checklists, which one can use to mark off accomplishments and see very clearly the direction and purpose of the next step. When I started in this work three years ago, I began to create an assessment of student learning handbook for department heads. I included three papers as critical background reading: "Assessment Measures and Methods: Advice from NCA," by Cecilia Lopez (North Central Association Commission on Institutions of Higher Education: Opportunities for Improvement: Advice from Consultant-Evaluators on Programs to Assess Student Learning, March 1996); "Assessment of Student Achievement: Levels of Implementation," from NCA's Handbook of Accreditation, 2nd edition (March 2000); and "Moving from Paperwork to Pedagogy: Channeling Intellectual [End Page 138] Curiosity into a Commitment to Assessment," by Peggy Maki, former director of assessment for the American Association for Higher Education (AAHE Bulletin, May 2002, pp. 3-5). In addition, I highly recommended Mary Huba and Jann Freed's Learner-Centered Assessment on College Campuses (Boston, MA: Allyn and Bacon, 2000), itself ahandbook of sorts for faculty who are beginning to develop a "culture of evidence" within their degree programs. Now, in the fall of 2004, I am recommending to the provost that every College Assessment Committee have a copy of Outcomes Assessment in Higher Education. This book offers four things: (1) a review of the mandate for the kind of accountability that is addressed by assessment of student learning processes (Chapter 4, "Institutional Assessment Planning"); (2) a series of very practical and concise "how-to" instructions that reflect what I have seen work (Chapter 5, "Developing an Assessment Plan to Learn about Student Learning"); (3) some models of specific approaches that share with us some of the barriers that were encountered and overcome along the way to effective change (Chapter 9, "Evolving an Assessment of Impact on Pedagogy, Learning, and Library Support of Teaching with Digital Images" and Chapter 10, "Outcomes Assessment in a College Library: An Instructional Case Study"); and (4) a summary of the progress that has been made in colleges and universities across the country so that we have a context in which to evaluate our progress (Chapter 3, "A Decade of Assessing Student Learning: What We Have Learned, and What is Next"). One of the...

In response to the increasing demand for graduates who are not only proficient in their field but also possess critical thinking skills and global competence, this study investigates the impact of innovative assessment practices on English as a Foreign Language (EFL) learners in Algerian higher education institutions. With the evolving landscape of education, traditional assessment methods are being increasingly replaced by more dynamic, learner-centered approaches aimed at fostering 21st century skills. This research employs a descriptive mixed-methods design, combining quantitative and qualitative data to explore the perceptions of 20 EFL instructors and 150 students across five Algerian universities. The study focuses on alternative assessment strategies such as project-based learning, digital portfolios, and peer assessments. Through a combination of student questionnaires, instructor interviews, and document analysis of student portfolios and project work, the research investigates the effects of these innovative methods on student engagement, critical thinking, and the development of global competence. The results reveal the positive influence of non-traditional assessments on learner motivation, analytical abilities, and intercultural awareness. Additionally, the study assesses the challenges faced by instructors and institutions in implementing these approaches and provides recommendations for enhancing their effectiveness. The findings suggest that innovative assessment practices play a crucial role in preparing EFL students for the demands of the globalized world, contributing to both their academic success and their ability to engage meaningfully in diverse, international contexts.

The need for quality assessment in higher education, particularly in the European Union countries is explained. Four main systems of evaluation having many elements in common but also many differences have emerged in four of the European Union countries. The purpose of the pilot projects described is to reinforce cooperation among existing national evaluation agencies and to disperse and disseminate these methods to the other European Union countries so as to enhance awareness of the need for quality assessment and evaluation in higher education, impart a European dimension to quality assessment and evaluation, enrich national quality assessment procedures, and contribute to the improvement of the basis and the procedures for academic mobility and recognition. The procedures for setting up and conducting pilot evaluation projects in selected higher education institutions in the European Union countries over a period running from November 1994 to December 1995 are detailed. * This article originally appear...

In the context of escalating global polycrises, residential high-rises face unprecedented challenges that threaten infrastructure and occupant safety. This literature review examines the role of Artificial Intelligence (AI) in managing these crises within vertical communities. Covering studies from 2014 to 2024, the review utilized a comprehensive search strategy across Web of Science, Scopus, IEEE Xplore, Google Scholar, and PubMed, focusing on AI, polycrisis, and high-rise buildings. The analysis identified 87 relevant studies showcasing the potential of AI technologies — such as machine learning, natural language processing, and computer vision — to enhance crisis management. Key findings indicate that AI can significantly improve emergency preparedness, real-time information dissemination, and resource optimization. Computer vision advances hazard detection accuracy, facilitating faster evacuations and better safety. The integration of IoT sensor networks with predictive analytics reduces false alarms and enhances early threat detection. However, the review also highlights challenges, including data privacy, cybersecurity, scalability, and the balance between AI autonomy and human oversight. Future research should address these limitations by focusing on real-world case studies to assess AI performance across diverse crisis scenarios. Development of more robust AI models and their integration with existing crisis management frameworks is essential. Ethical considerations, particularly regarding privacy and bias, must also be scrutinized to ensure that AI solutions are both effective and equitable. This review provides valuable insights into using AI to bolster urban resilience and support Sustainable Development Goals in high-rise residential settings, emphasizing the need for strategic AI integration and ongoing research to address current gaps and challenges.