Academic Quality Management System Audit Using Artificial Intelligence Techniques

Bojorque, Rodolfo; Pesántez-Avilés, Fernando

doi:10.1007/978-3-030-20454-9_28

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…In addition, it has been demonstrated that AI can improve learning management systems' functionality and anticipate learners' needs [57]. According to Bojorque [8], artificial intelligence can process structured and unstructured data, reducing the work management must do and accelerating the decision-making process.…”

Section: Ai Impact On Education Qualitymentioning

confidence: 99%

Systematic Review: AI's Impact on Higher Education - Learning, Teaching, and Career Opportunities

Slimi,

Carballido

2023

TEM Journal

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AI is transforming many fields, including higher education. The pandemic has shown how AI can improve learning and teaching in higher education. This review examines how AI affects education quality, learning assessment, and higher education jobs (HE). The study employs a systematic qualitative method to review the academic literature on AI and higher education between 1900 and 2021. The data was gathered from various sources, including ERIC, Scopus, and the Web of Science, using specific exclusion and inclusion criteria centred on publication date, language, reported outcomes, setting, and publication type. From there on, the articles were analysed by Rayyan Software and categorised in Excel according to a scale that included aspects such as the quality of learning and teaching, assessment, and potential ethical future careers. The research also produced two bibliometric figures using VOSviewer to investigate co-authorship and the frequency of keyword occurrences in academic journals published in AI and HE. The analysis was done to ensure the study's validity in the scientific community. The study found that AI can improve education quality, provide practical learning and teaching methods, and improve assessments to better prepare students for careers. The study also emphasises the potential of AI to shape future employment opportunities and the need for higher education institutions to adopt AI to meet market demands. The study calls for more research on AI's effects on assessment, integrity, and higher education careers.

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Section: Ai Impact On Education Qualitymentioning

confidence: 99%

Systematic Review: AI's Impact on Higher Education - Learning, Teaching, and Career Opportunities

Slimi,

Carballido

2023

TEM Journal

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“…AI has penetrated medical science, education, finance, agriculture, industry, security and many other sectors (Jha et al, 2019). Knowing AI techniques and applications can help management reduce the time in taking a decision (Bojorque and Pes antez-Avil es, 2020). AI has an expansive series of tasks it can accomplish (Dejoux and L eon, 2018).…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education

Okunlaya

Abdullah

Alias

2022

LHT

133

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PurposeArtificial intelligence (AI) is one of the latest digital transformation (DT) technological trends the university library can use to provide library users with alternative educational services. AI can foster intelligent decisions for retrieving and sharing information for learning and research. However, extant literature confirms a low adoption rate by the university libraries in using AI to provide innovative alternative services, as this is missing in their strategic plan. The research develops (AI-LSICF) an artificial intelligence library services innovative conceptual framework to provide new insight into how AI technology can be used to deliver value-added innovative library services to achieve digital transformation. It will also encourage library and information professionals to adopt AI to complement effective service delivery.Design/methodology/approachThis study adopts a qualitative content analysis to investigate extant literature on how AI adoption fosters innovative services in various organisations. The study also used content analysis to generate possible solutions to aid AI service innovation and delivery in university libraries.FindingsThis study uses its findings to develop an Artificial Intelligence Library Services Innovative Conceptual Framework (AI-LSICF) by integrating AI applications and functions into the digital transformation framework elements and discussed using a service innovation framework.Research limitations/implicationsIn research, AI-LSICF helps increase an understanding of AI by presenting new insights into how the university library can leverage technology to actualise innovation in service provision to foster DT. This trail will be valuable to scholars and academics interested in addressing the application pathways of AI library service innovation, which is still under-explored in digital transformation.Practical implicationsIn practice, AI-LSICF could reform the information industry from its traditional brands into a more applied and resolutely customer-driven organisation. This reformation will awaken awareness of how librarians and information professionals can leverage technology to catch up with digital transformation in this age of the fourth industrial revolution.Social implicationsThe enlightenment of AI-LSICF will motivate library professionals to take advantage of AI's potential to enhance their current business model and achieve a unique competitive advantage within their community.Originality/valueAI-LSICF development serves as a revelation, motivating university libraries and information professionals to consider AI in their strategic plan to enable technology to support university education. This act will enable alternative service delivery in the face of unforeseen circumstances like technological disruption and the present global COVID-19 pandemic that requires non-physical interaction.

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“…Evaluating education quality is crucial for understanding the education system's effectiveness in fostering students' cognitive development, instilling values and attitudes, ad-dressing local and global challenges, nurturing creativity and emotional growth, and fostering analytical problemsolving skills [4] [5]. The quality of education is extremely complex because it involves many stakeholders with varying perspectives: the government, employers, academics, students, parents, and society at large, all of whom describe excellence differently [6]. However, the majority of universities globally present quality criteria or standards of education, such as educational activities, and the analysis of this can be evaluated through the following aspects: programmes/curriculum, assessments, admission system, and other resources [7], of which curriculum and assessment, i.e., examinations, are taken as a focal point for this study.…”

Section: Introductionmentioning

confidence: 99%

A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data

Ahidi Elisante Lukwaro,

Kalegele,

G. Nyambo

2024

IJCDS

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There has been a significant increase in academic processes to ensure the quality of educational resources such as curricula, examinations, and educational content. This has drawn attention to studies exploring the use of text mining, learning machines, and auto-analytic tools like natural language processing (NLP) to interpret and evaluate the quality of these educational resources. Auto-analytical techniques are required to evaluate the quality of educational content; otherwise, manual evaluation can be burdensome and improperly influenced by human instincts. This study employs a methodical approach to comprehensively survey NLP techniques for extracting syntactic and semantic features to analyze and comprehend educational content. NLP, in combination with machine learning, is an ideal tool for automatically evaluating the aspects of higher education quality. This is because they include features that aid in textual content comprehension as well as implementing natural language techniques that provide an interpretive interface between humans and machines. The review highlights the limitations of NLP in evaluating educational data, including the need for sentence-level understanding and the need for research to address challenges like noise in text data, domain-specific language variations, and improving model robustness for effective feature extraction in educational contexts. The findings of this review hold substantial benefits for various stakeholders, including education regulatory bodies, researchers, higher education institutions, and NLP researchers. Notably, the study equips NLP researchers with valuable insights into document analysis's current strengths and weaknesses. The accumulated evidence can provide the skills to develop NLP-based applications for evaluating the relevant and quality aspects of education in higher educational settings. Furthermore, NLP researchers can be updated on the strengths and limitations of document analysis, allowing them to apply effective text representation approaches and implement the appropriate algorithm and techniques for NLP tasks, particularly in educational data.

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Academic Quality Management System Audit Using Artificial Intelligence Techniques

Cited by 11 publications

References 9 publications

Systematic Review: AI's Impact on Higher Education - Learning, Teaching, and Career Opportunities

Systematic Review: AI's Impact on Higher Education - Learning, Teaching, and Career Opportunities

Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education

A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data

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