Assessment on integration of Artificial intelligence in Assessment and Research activities among University Lecturers in South-South, Nigeria

Relevance. The objectivity of assessing the professional activities of pedagogical staff is a pressing task in modern education. This is due to dynamic changes in the organization of the educational process and increasing demands on the professional competence of pedagogical staff. Traditional methods of assessing the professional activities of pedagogical staff are often subjective and do not fully reveal their level of knowledge, skills, and abilities. In this context, artificial intelligence offers tools for a more accurate, transparent, and comprehensive assessment of the professional activities of pedagogical staff based on the analysis of data on teaching quality, their interaction with general secondary education students, and learning outcomes. Purpose. The purpose of the article is to investigate the role of artificial intelligence in enhancing the objectivity of assessing the professional activities of pedagogical staff in general secondary education institutions. Methods. The research methods included: studying scientific sources and regulatory documents concerning the use of artificial intelligence technologies in education to identify the state of research on the problem; theoretical analysis, synthesis, and generalization of views to substantiate the role of artificial intelligence in the objective assessment of the professional activities of pedagogical staff in general secondary education institutions; and generalization of findings. Results. The article substantiates the role of artificial intelligence in the objectivity of assessing the professional activities of pedagogical staff in general secondary education institutions based on the use of big data processing algorithms and comprehensive analytics, automation of the collection and analysis of quantitative and qualitative indicators of pedagogical staff's professional activities, the structure of their interaction with students, analysis of competency development dynamics, learning materials (through natural language processing), and student learning outcomes. The features of using artificial intelligence in assessing the professional activities of pedagogical staff are revealed through task personalization and the provision of individual recommendations for their further professional development. Key advantages and challenges associated with the use of artificial intelligence in assessing the professional activities of pedagogical staff in general secondary education institutions are identified. Conclusions. The study found that the use of artificial intelligence significantly enhances the objectivity, efficiency, and transparency of assessing the professional activities of pedagogical staff in general secondary education institutions, as it allows for a shift from quantitative assessment criteria to comprehensive analysis and contributes to the formation of individual trajectories for their professional growth. Successful implementation of artificial intelligence in assessing the professional activities of pedagogical staff is based on considering unified methodological approaches and standards, as well as the availability of appropriate technical infrastructure.

Abstract: The application of Artificial Intelligence (AI) in learning assessment has attracted the attention of many educational experts, researchers and practitioners. This study discusses the opportunities and challenges of using AI in learning assessment. Traditional assessment has weaknesses in terms of misjudgment, inability to measure individual abilities that are not measured in certain forms of assessment, significant cost and time, slow feedback, and inability to be adjusted individually. Several studies have shown that the use of AI in assessments can improve the accuracy, validity and reliability of assessments, reduce human rater bias, enable adaptive assessments, increase time and cost efficiency, provide faster and more timely feedback, and assist in identifying individual needs and improve the quality of learning. However, the use of AI technology can only be a tool, and the final decision must still be made by humans. Therefore, the use of AI in assessment requires special attention in terms of ethics and the development of human capabilities to understand and use AI technology wisely.Keywords: Artificial Intelligence, Assessment Abstrak: Penerapan Artificial Intelligence (AI) dalam penilaian pembelajaran telah menarik perhatian banyak ahli pendidikan, peneliti, dan praktisi. Penelitian ini membahas peluang dan tantangan penggunaan AI dalam asesmen pembelajaran. Asesmen tradisional memiliki kelemahan dalam hal kesalahan penilaian, ketidakmampuan mengukur kemampuan individu yang tidak terukur dalam bentuk asesmen tertentu, biaya dan waktu yang signifikan, umpan balik yang lambat, dan ketidakmampuan untuk disesuaikan secara individual. Beberapa penelitian menunjukkan bahwa penggunaan AI dalam asesmen dapat meningkatkan akurasi, validitas, dan reliabilitas asesmen, mengurangi bias penilai manusia, memungkinkan asesmen adaptif, meningkatkan efisiensi waktu dan biaya, memberikan umpan balik yang lebih cepat dan tepat waktu, serta membantu dalam mengidentifikasi kebutuhan individu dan meningkatkan kualitas pembelajaran. Namun, penggunaan teknologi AI hanya dapat menjadi alat bantu, dan keputusan akhir tetap harus dilakukan oleh manusia. Oleh karena itu, penggunaan AI dalam asesmen memerlukan perhatian khusus dalam hal etika dan pengembangan kemampuan manusia dalam memahami dan memanfaatkan teknologi AI dengan bijak.Kata Kunci: Kecerdasan Buatan, Asesmen

This study aims to analyze the effectiveness of applying Artificial Intelligence (AI) in assessment gamification to improve student learning outcomes at SDN Kemayoran 2 Bangkalan. The research employed a quasi-experimental method with a nonequivalent control group design. The participants consisted of fourth-grade students divided into a control class and an experimental class. The instruments used included pretest and posttest assessments to measure students’ understanding before and after the intervention, as well as data analysis using an independent samples t-test, normality test, and N-Gain test. The study results indicated an increase in the average learning outcome scores for both the control group (81.68) and the experimental group (86.63), with the latter showing a significantly greater improvement. The independent samples t-test yielded a significance value of 0.00 (< 0.05), confirming data were accepted. Furthermore, the normality test indicated that the data were normally distributed, as the significance value was ≥ 0.05. Additionally, the N-Gain test yielded an average score of 0.80 (high improvement category) and an effectiveness percentage of 80.41% (highly effective category). Therefore, the study concludes that the application of Artificial Intelligence in assessment gamification is proven effective and has a substantial impact on improving student learning outcomes at SDN Kemayoran 2 Bangkalan. This research implies that AI-based educational technologies can serve as innovative alternatives to support the learning process in elementary schools.

There is emerging evidence which suggests the utility of artificial intelligence (AI) in the diagnostic assessment and pre-treatment evaluation of thyroid eye disease (TED). This scoping review aims to (1) identify the extent of the available evidence (2) provide an in-depth analysis of AI research methodology of the studies included in the review (3) Identify knowledge gaps pertaining to research in this area. This review was performed according to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA). We quantify the diagnostic accuracy of AI models in the field of TED assessment and appraise the quality of these studies using the modified QUADAS-2 tool. A total of 13 studies were included in this review. The most common AI models used in these studies are convolutional neural networks (CNN). The majority of the studies compared algorithm performance against healthcare professionals. The overall risk of bias and applicability using the modified Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool led to most of the studies being classified as low risk, although higher deficiency was noted in the risk of bias in flow and timing. While the results of the review showed high diagnostic accuracy of the AI models in identifying features of TED relevant to disease assessment, deficiencies in study design causing study bias and compromising study applicability were noted. Moving forward, limitations and challenges inherent to machine learning should be addressed with improved standardized guidance around study design, reporting, and legislative framework.

The use of technologies is intended to improve the processes of knowledge dissemination among the lecturers in universities in the modern age. The research was at the cross-section between open science project and sharing of knowledge among lecturers in public universities Southwestern Nigeria. A descriptive survey research design was used. The sample population of the study included 227 lecturers in the public universities in Southwestern Nigeria through the purposive sampling method with 12 universities being selected of 3 states that comprise Ekiti, Ondo and Osun States, Nigeria. An online questionnaire using WhatsApp was used to collect data and analyse the data concerning the research questions with the descriptive statistics. The findings revealed low awareness and usage of open science initiatives to disseminate knowledge among the lecturers with the average mean (=2.49). The implementation of open science initiatives was crippled by rigorous peer review process and poor funding of research. The study found a positive significant relationship between awareness and knowledge dissemination using open science initiatives among the lecturers in the public universities in the selected state, Nigeria (r =.429; df=221; p=0.01). In conclusion, the research concluded that the awareness of open science initiatives among lecturers in public universities in selected state, Nigeria was low. Although they know about Directory of Open Access Journals (DOAJ), research data management training, open data repositories, and Open Access Scholarly Publishers Association (OASPA). It was recommended that university management ought to formulate policies that will promote the implementation of open science initiatives in knowledge dissemination among lecturers in public universities in Southwestern Nigeria.

Assessment is a crucial aspect of educational development. There are growing concerns among the academia that the traditional assessment is not holistic and may not reflect students' academic achievement. Artificial intelligence (AI) which can capture students’ backgrounds and activities is being used in assessing their academic achievement in some universities across the globe. However, there are arguments that the use of artificial intelligence (AI) in assessing students’ academic achievement may be novel to lecturers in Nigerian universities. The paper examined the application of AI in the assessment of student’s academic achievement in the private university system in the Southeast Zone of Nigeria. Three research questions guided the study. A mixed methods research design was adopted for the study. The sample constituted 72 lecturers from eight (8) private universities in the southeastern states of Nigeria. Two instruments: Questionnaire on the Application of AI in Assessment (QAAIA) and Smartphone Audio Recorder (SAR) were used for data collection. The QAAIA was validated by experts and a Cronbach Alpha reliability coefficient of 0.79 was established. Quantitative data were analysed using descriptive statistics of mean and standard deviation while qualitative data were analysed thematically. It was found that the AI facilitates holistic assessment of students’ academic achievement. The result also revealed that the level of awareness of lecturers on the application of AI in assessment is high. However, most lecturers do not apply AI in the assessment of their students. It is recommended among others, that universities in the South East zone of Nigeria should train a critical mass of staff on applications of AI in solving educational assessment issues.

This chapter explores the integration of Artificial Intelligence (AI) in education for formative and summative assessment. The educational shift from teacher-based assessment to AI-powered assessment tools have evolved to increase the learning outcomes for the students and helped teachers to implement best assessment practices. The relevance of AI in assessment has created a balanced approach to improve assessment in education. Moreover, AI-powered tools have created new landscapes for students' assessment for both formative as well as summative assessments. The chapter discusses the role of AI in student assessment such as formative and summative assessment with AI, personalized learning paths, adaptive learning, instant feedback, tracking progress, Integration of AI in Pedagogy, and Behavioral and Engagement Analytics to improve students learning outcomes. Additionally, it addresses the challenges in implementing AI-powered assessment for efficient resources allocation, and fairness, bias, and ethical consideration in AI algorithms.

The article focuses on ways to use artificial intelligence in assessment and enhancement of creativity. This topic seems very important in the context of the intensive development of computer technologies providing people with the vast range of opportunities to improve their professional skills and intensify their personal development. Some particular ways of the use of artificial intelligence are analyzed. Artificial intelligence can operate independently and generate its own creative ideas. At the same time, it can interact with humans within the creative process or serve as a “creative assistance” of humans. The results of the empirical studies in this area showed that the efficacy of the artificial intelligence in the course of assessment and enhancement of human creativity is determined to considerable extent by a task given, a particular area which artificial intelligence operates in, and the specific forms of its interactions with humans. In some areas (e.g., generation of alternative uses), artificial intelligence can outperform humans, whereas in other tasks (e.g., creative writing) humans perform better that artificial intelligence. Some practical recommendations on how to optimize the use of artificial intelligence in assessment and enhancement of creativity, were proposed. Results of the study can be used in the development of creativity assessment methods as well as for the improvement of interaction between people and artificial intelligence.

The introduction of databases by university libraries has presented lecturers with opportunities of obtaining accurate, timely and up-to-date information with little effort. However, research reports have revealed that there is low level of awareness of electronic databases by university lecturers. Hence this study investigated availability and awareness of electronic databases for teaching and research by lecturers in public universities in South-west, Nigeria. The objectives of the study were to: ( I ) identify the types of databases available to lecturers in public universities in South-west, Nigeria; and (ii) examine the extent of awareness of available databases for teaching and research by university lecturers in South-west, Nigeria. The study adopted the descriptive research design of a correlational type. The population comprised 10,452 lecturers in fifteen public universities in South-west, Nigeria from which a sample size of 836 was drawn using a multi-stage sampling procedure. Questionnaire was used as instrument for data collection. Data were analysed using descriptive and inferential statistics at 0.05 level of significance. Findings of the study revealed that numerous electronic databases were available in public university libraries in South-west, Nigeria and that university lecturers’ level of awareness of most of the electronic databases for teaching and research was above average (60.6%) as against below average reported in the literature. It can be concluded from the study that the university libraries in South-west, Nigeria are not creating much awareness of their electronic databases. It is therefore recommended that university libraries, especially in South-west, Nigeria should intensify their promotional activities geared towards marketing their electronic databases.

Objectives: A systematic review of the literature was undertaken (1) to investigate research trends on how artificial intelligence is being used for assessment and diagnosis in the field of communication disorders and (2) to suggest consideration and a directions for the effective use of artificial intelligence in clinical settings. Methods: A total of 328 articles published in foreign journals between January 2016 and August 2021 were searched using 6 databases and a manual search, and 18 articles were finally selected according to PICO strategy (Population, Intervention, Comparison, Outcome) inclusion and exclusion criteria. Four authors determined the report selection and data extraction. They also independently analyzed the quality of the selected papers using QUADAS-II (Quality Assessment of Diagnostic Accuracy Studies-II). Results: Firstly, the selected studies had a generally low risk of bias. Secondly, the major subjects of studies were children with communication disorders. Thirdly, most of the studies included in the analysis were experimental studies to verify the effectiveness of using artificial intelligence. Lastly, the extracted features for assessment and diagnosis were biased against acoustic features at the levels of phoneme and word in speaking tasks. The performance of artificial intelligence in the selected studies differed according to the research purpose and evaluation metrics. Conclusion: This study suggests that in order for artificial intelligence to be used in the assessment and diagnosis system, it is essential to acquire clinically reliable and high-quality big data on the characteristics of speech and language of people with communication disorders.

Correction to: Artificial intelligence in assessment of hepatocellular carcinoma treatment response.

Purpose: With medical advancements contributing to increased life expectancy, the growing geriatric population has heightened the need for speech–language pathology services to address neurogenic communication impairments. Artificial intelligence (AI) offers promising opportunities to augment care and meet this rising demand. The purpose of this review was to synthesize current evidence on the use of AI in assessing neurogenic communication disorders in older adults. By focusing on clinical applications such as automatic speech recognition (ASR) and related AI tools, this review highlights their potential to improve efficiency, accessibility, and accuracy in assessment, while also addressing challenges to successful clinical integration. Materials and Methods: A literature review was performed using the keywords AI, ASR, aphasia, apraxia, and dysarthria on several databases, including PubMed, Google Scholar, and ASHA Journals Academy. Results: This review summarizes current attempts at incorporating AI in automating the detection and diagnosis of neurogenic communication disorders, including dysarthria, aphasia, and apraxia of speech. Conclusion: AI shows promise in the speech therapy field, in assisting with screening and evaluation of neurogenic communication disorders. However, clinical integration of these tools is challenging given their limitations with culturally and linguistically diverse datasets and concerns with ethical bias and data privacy.

Artificial intelligence (AI) is a broad term that refers to the use of computers to replicate intelligent behavior with minimal human intervention. AI is rapidly transforming various sectors, including speech and language pathology, by offering innovative solutions to enhance therapeutic practices and client outcomes. Its application in speech and language pathology spans several domains, including medical diagnosis, therapeutic planning, and rehabilitation, utilizing tools such as machine learning and deep learning to enhance data analysis and pattern recognition. The primary aim of this study is to provide resources for speech and language pathologists on the topic of artificial intelligence by presenting research findings on the assessment and intervention of speech and language disorders using AI. Accordingly, AI studies in speech and language pathology found in the literature were included. The results of these studies were summarized, and information was provided on the use of AI in assessing and treating speech and language disorders, including swallowing disorders, voice disorders, acquired language disorders, motor and speech sound disorders, cleft palate speech, and developmental language disorder. Existing literature acknowledges and supports the growing popularity of AI and AI-based algorithms in speech and language pathology. Although the current evidence remains insufficient and concerns about ethics and implementation persist, advancing technology offers promise for applying AI in this field.

Assessment for Learning (AfL) is a process in measuring the learning outcome in students. Current practices in assessing the academic performance of students in most of the countries are still manual. It is based on the qualitative and quantitative feedbacks, obtained by expressed statement and marks, respectively. The issues associated with such assessment-practices are that it (a) lacks autonomy in students and the teachers to assess themselves for (1) better learning (ABeL) and (2) to learning (AtoL) with greater accuracy; (b) Self, peer and parents' involvements in the assessment process has often been underestimated, and (c) involved human bias while giving the qualitative and quantitative feedbacks. Given the background, this chapter attempts to showcase how various Artificial Intelligence (AI)-based solutions, such as Expert Control System (ECS)-based tutoring platform and Agent-based tutoring systems (AbS) can be used for the AfL, which in turn, improve ABeL and AtoL in students.

Artificial Intelligence in Assessment of Students' Performance