The effectiveness of artificial intelligence in gamification assessment for meansuring learning outcomes of elementary school students at SDN Kemayoran 2 Bangkalan

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

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.

This study focused on Ethical and Intellectual Considerations in the Deployment of Artificial Intelligence in Educational Assessment and Research among Social Studies Lecturers in Universities in south-south, Nigeria. Three research questions and 3 hypotheses guided the study. The study adopted a descriptive survey research design. The study comprised of 120 Social Studies lecturers in public federal and State universities in south-south, Nigeria. A structured questionnaire was used for data collection. Data were analyzed using descriptive statistics (mean and standard deviation) to summarize responses and t-test statistics to test the hypotheses. The findings revealed among others that there was significant difference in the level of awareness regarding the responsible and ethical use of AI technologies between Social Studies lecturers in Federal and State universities. The study concluded that although AI tools offer innovative prospects for educational advancement, there is a pressing need to enhance ethical awareness and address intellectual property challenges among Social Studies lecturers. It was recommended that universities and educational authorities should organize regular training workshops, develop clear ethical guidelines, and promote responsible use of AI tools in research and assessment

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 study aims to determine the effect of the implementation of the Project Based Learning (PjBL) learning model through a differentiation approach in improving creativity and learning outcomes of students in class XI of SMA Negeri 13 Samarinda. This research method uses a quantitative approach with a non-equivalent control group design. The population in this study was all class XI of SMA Negeri 13 Samarinda. Sampling used a purposive sampling technique. The sample in this study was class XI Moving Biology (XI-5 and XI-8) as an experimental class using the Project Based Learning (PjBL) learning model through a differentiation approach and XI-3 as a control class using a conventional learning model. Data were collected through tests in the form of essay questions and observation sheets. The data from the test questions and observation sheets were analyzed using an independent sample t-test and an N-gain test with the help of a data processing application. The statistical test results obtained from the independent sample t-test on creativity showed a significance value of 0.000 < 0.05, as well as the independent sample t-test on learning outcomes of 0.011 < 0.05, so H0 was rejected and Ha was accepted. It can be concluded that there is an influence of the application of the Project Based Learning (PjBL) learning model through a differentiation approach on creativity and learning outcomes of students in the experimental class. Furthermore, the N-gain test showed an increase in student learning outcomes after implementing the Project-Based Learning (PjBL) model through a differentiation approach, with an N-gain score of 0.91, in the high category. These research results indicate that implementing PjBL with a differentiation approach can be an effective alternative learning strategy for developing creativity and improving student learning outcomes in high school.

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.

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.

To compare the item difficulty and discriminative index of multiple-choice questions (MCQs) generated by ChatGPT with those created by dental educators, based on the performance of dental students in a real exam setting. A total of 40 MCQs-20 generated by ChatGPT 4.0 and 20 by dental educators-were developed based on the Oral Diagnosis and Radiology course content. An independent, blinded panel of three educators assessed all MCQs for accuracy, relevance and clarity. Fifth-year dental students participated in an onsite and online exam featuring these questions. Item difficulty and discriminative indices were calculated using classical test theory and point-biserial correlation. Statistical analysis was conducted with the Shapiro-Wilk test, paired sample t-test and independent t-test, with significance set at p < 0.05. Educators created 20 valid MCQs in 2.5 h, with minor revisions needed for three questions. ChatGPT generated 36 MCQs in 30 min; 20 were accepted, while 44% were excluded due to poor distractors, repetition, bias, or factual errors. Eighty fifth-year dental students completed the exam. The mean difficulty index was 0.41 ± 0.19 for educator-generated questions and 0.42 ± 0.15 for ChatGPT-generated questions, with no statistically significant difference (p = 0.773). Similarly, the mean discriminative index was 0.30 ± 0.16 for educator-generated questions and 0.32 ± 0.16 for ChatGPT-generated questions, also showing no significant difference (p = 0.578). Notably, 60% (n = 12) of ChatGPT-generated and 50% (n = 10) of educator-generated questions met the criteria for 'good quality', demonstrating balanced difficulty and strong discriminative performance. ChatGPT-generated MCQs performed comparably to educator-created questions in terms of difficulty and discriminative power, highlighting their potential to support assessment design. However, it is important to note that a substantial portion of the initial ChatGPT-generated MCQs were excluded by the independent panel due to issues related to clarity, accuracy, or distractor quality. To avoid overreliance, particularly among faculty who may lack experience in question development or awareness of AI limitations, expert review is essential before use. Future studies should investigate AI's ability to generate complex question formats and its long-term impact on learning.

The abstract concept of energy transformation is a subject in the natural and social sciences that students frequently find difficult to comprehend. Conventional learning methods are less effective in enabling students to actively engage in the learning process and visualize energy transformation concepts. Consequently, students encounter challenges in comprehending the energy transformation process, the relationship between energy, and examples of objects. The goal of this study is to evaluate how effective problem-based learning, which includes PhET simulations, gamified quizzes, and songs, is in helping fourth-grade students at SD Negeri Losari better understand energy transformation concepts. The quasi-experimental method was employed in this study, with a nonequivalent control-group design. The experimental class IV A consisted of 21 students, while the control class IV B had 20 students. The total number of students in the sample was 41. The total sampling technique was employed in conjunction with nonprobability sampling. Teaching was conducted in the experimental class (IVA) through PhET simulation, gamification, problem-based learning, and melodies. In contrast, the control group (IV B) implemented conventional teaching methods in conjunction with instructional videos. The independent sample t-test and N-Gain test were the methods of statistical analysis. A significant improvement in learning outcomes was confirmed by the independent t-test, which yielded a p-value of less than 0.05. In contrast to the control class, which achieved an average N-Gain score of 0.2678 (low), the experiment class achieved an average N-Gain score of 0.6655 (moderate). This finding suggests that the conceptual comprehension of energy transformation is enhanced through the incorporation of gamified quizzes, songs, PhET simulations, and problem-based learning. Keywords: PhET simulations, problem-based learning, song, quizzez, energy transformation.

This study aimed to examine the effect of the Scramble-type cooperative learning model on students’ learning outcomes in Pancasila Education among fourth-grade students at SDN 1 Lambheu Aceh Besar. The research employed a quantitative approach with a quasi-experimental method using a nonequivalent control group design. The population consisted of all fourth-grade students, with the sample comprising an experimental class and a control class selected through purposive sampling. Data were collected using a 20-item multiple-choice test administered as pretest and posttest. The data were analyzed using the N-Gain test, normality and homogeneity tests, and hypothesis testing through an independent sample t-test with the assistance of SPSS. The findings revealed that students taught using the Scramble-type cooperative learning model achieved higher learning outcomes compared to those taught using the direct learning model. The hypothesis testing showed a significance value (2-tailed) &lt; 0.05, indicating that Ha was accepted and Ho was rejected. Therefore, it can be concluded that the Scramble-type cooperative learning model has a significant effect on the learning outcomes of fourth-grade students in Pancasila Education at SDN 1 Lambheu Aceh Besar.

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.

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.

This study aims to determine the effectiveness of Learning Together (LT) type cooperative learning model on learning outcomes and creativity of students on acid-base material in class XI MIPA SMA Muhammadiyah 3 Yogyakarta. The population of this study were all students of class XI MIPA SMA Muhammadiyah 3 Yogyakarta which amounted to 93 students and divided into 3 classes, namely class XI MIPA 1, XI MIPA 2, and XI MIPA 3. The samples of this study were class XI MIPA 2 as the control class and class XI MIPA 3 as the experimental class, each class consisting of 31 students. This research method is a quasi-experiment. The design used is nonequivalent control group design. Data collection techniques using test questions and filling out questionnaire sheets of students’creativity. Data analysis techniques used are normality test, homogeneity test, and hypothesis testing (Mann Whitney test and independent sample t-test). Based on the results of the Mann Whitney test analysis, the Sig (2-tailed) value of 0.000 &lt;0.05 was obtained, so H0 rejected and H1 was accepted, meaning that the Learning Together (LT) type cooperative learning model was effective on learning outcomes on acid-base material. Based on the results of the independent sample t-test analysis, the Sig. 0.005 &lt; 0.05, so that H0 rejected and H1 was accepted, meaning that the Learning Together (LT) type cooperative learning model is effective on students' creativity in acid-base material.

Learning method is a way that is taken or used by the teacher to deliver a learning material in an effort to achieve the goals of a learning. Whereas lPA is one of the lessons that must involve students directly with the implementation of practicums to improve students' skills. In the process of learning science at SDN Tlogoagung, researchers found that learning outcomes were still low, the average score was 60% lower than the specified KKM, which was 75. This was possible because of the conventional learning process. The focus of the research that will be discussed in this study is about how the effectiveness of the inquiry learning method for learning outcomes and the skills of the science learning process in the matter of energy and its changes. The purpose of this study was to determine the effectiveness of the method of inquiry learning on learning outcomes and the skills of the science learning process on energy matter and its changes. The method used in this study is an experimental method using nonequivalent control group design. Data collection techniques used were interviews, tests, observation sheets and documentation. While the data analysis used is the validity test, reliability test, normality test, homogeneity test, assessment of the test, mean, achievement of learning outcomes, N-Gain, observation of basic process skills, and hypothesis testing using Independent samples t-test. From the results of the study it is known that the use of inquiry learning methods seen from the results of learning between the experimental class with the control class, while the average value of the experimental class post test is 87 while the average value of the control class post test is 79. Based on the results of hypothesis testing indicates that the value sig (2 tailed) smaller than 0.05 so Ho is rejected and Ha is accepted. This means that the inquiry learning method is effective on learning outcomes and the skills of the science learning process on energy matter and its changes.