Artificial Intelligence-Based Decision Support System for Education Management in Higher Education
Yohanes Bowo Widodo 1, Renny Ursula Korwa 2, Rini Nuraini 3
1 Universitas Mohammad Husni Thamrin Jakarta, Indonesia
2 IISIP YAPIS Biak Papua, Indonesia
3 Universitas Nasional, Indonesia
Corresponding Author: Yohanes Bowo Widodo, E-mail; [email protected]
Article Information:
Received December 10, 2023 Revised December 19, 2023 Accepted December 25, 2023
ABSTRACT
Universities face challenges in improving the effectiveness of lecturer performance evaluation to support education and research development.
Artificial Intelligence (AI) offers the potential to optimize the evaluation process through automation, big data analysis, and smarter decision- making. This research aims to identify and implement strategies for the implementation of artificial intelligence in lecturer performance evaluation systems in higher education. The main focus is to improve the objectivity, accuracy, and efficiency of the evaluation process, so as to provide more meaningful feedback to lecturers. This research uses qualitative and quantitative approaches. Data were obtained through literature studies, interviews with education and technology experts, and analysis of existing lecturer performance evaluation data. An AI system was developed to process and analyze evaluation data by utilizing machine learning algorithms to provide more accurate evaluation recommendations. The result of this research is that implementing artificial intelligence in the lecturer performance evaluation system succeeded in improving the accuracy of the evaluation. The system is able to provide recommendations for lecturer development based on a thorough analysis of their performance. In addition, the time efficiency of the evaluation process was also significantly improved. The conclusion of this study shows that artificial intelligence implementation strategies can be successfully applied in lecturer performance evaluation systems in higher education. With increased objectivity, accuracy, and efficiency, this system makes a positive contribution to human resource management in the academic environment. It also encourages further implementation of AI technology in lecturer performance evaluation to improve the quality of higher education.
Keywords: Artificial Intelligence, Lecturer Performance, Higher Education
Journal Homepage https://ojs.iainbatusangkar.ac.id/ojs/index.php/alfikrah/index This is an open access article under the CC BY SA license
https://creativecommons.org/licenses/by-sa/4.0/
How to cite: Widodo, B, Y., Korwa, U, R & Nuraini, R. (2023). Artificial Intelligence Based
Jurnal Manajemen Pendidikan, 11(2), 352-365.
https://doi.org/10.31958/jaf.v11i2.12120
Published by: Universitas Islam Negeri Mahmud Yunus Batusangkar Press
INTRODUCTION
In the growing digital age, artificial intelligence (AI) technology has become one of the most prominent innovations shaping various aspects of human life (Central Queensland University, Queensland et al., 2022). It has led transformations in various industries, including education, healthcare, business and more. AI represents the ability of machines to mimic human intelligence, process data, learn from experience, and make complex decisions. With these capabilities, AI has become a key driver of change and innovation around the world (Xiao et al., 2022). As a rapidly evolving technology, AI encompasses various branches, including machine learning, deep learning, natural language processing, and computer vision. Machine learning allows machines to learn from data without direct programming, while deep learning adopts the structure of artificial neural networks to model and extract complex patterns. Natural language processing allows machines to understand and respond to human language, while computer vision gives machines the ability to process and understand visual information.
One of the most prominent applications of AI is in the field of artificial intelligence in performance evaluation, where the technology is used to optimize and improve the assessment process. The implementation of artificial intelligence in evaluation systems provides the potential to produce more accurate and efficient analysis, aiding better decision-making in a variety of contexts. In various sectors, including higher education, artificial intelligence is being highlighted as an innovative solution to evaluate and improve individual performance. In higher education, artificial intelligence can be applied in various aspects, including lecturer performance evaluation. Lecturer performance evaluation is a critical element in ensuring the quality of teaching, research, and other academic contributions. By utilizing artificial intelligence, evaluation systems can become more efficient and accurate, enabling decision-making based on more in-depth data analysis (Owoc et al., 2021a). However, along with its positive potential, the application of artificial intelligence in lecturer performance evaluation also raises various questions and challenges. Ethical aspects, privacy, and user acceptance of the technology are important considerations. Therefore, understanding and further exploring how artificial intelligence can be applied wisely and effectively in the context of lecturer performance evaluation is necessary.
Literatur of Refiew
1. Artificial Intelligence Implementation Strategy
The implementation of artificial intelligence (AI) in higher education is a significant challenge and opportunity to increase efficiency, quality and innovation in various aspects of education (Huang et al., 2007). AI implementation strategies in higher education require careful planning in order to provide maximum positive impact. One
of the main strategies in implementing artificial intelligence in higher education is identifying areas that can be optimized by this technology (Arantes, 2023). Higher education has various fields, such as administration, learning, research, and student services. Identifying areas that can benefit from the application of AI will help focus on solutions that can provide greater benefits (AlDhaen, 2022). In terms of administration, the use of chatbots to handle general questions from students and administrative staff can increase efficiency in service. Chatbots can provide real-time information and reduce the workload of administrative staff, so they can focus on more complex tasks (Amalia et al., 2020). AI-based data management systems can also help manage student, lecturer and administrative data more efficiently.
The application of artificial intelligence in the learning process is an important strategy to improve student learning experiences (Agarwal et al., 2022). Adaptive teaching systems can be tailored to students' individual levels of understanding and learning styles, providing a more personalized and effective learning experience (M.
Bauer et al., 2018). Automatic assessment with AI can also speed up the process of providing feedback, allowing lecturers and students to get feedback instantly (Sun et al., 2020). In the research aspect, artificial intelligence can be used for large and complex data analysis, helping researchers identify patterns, trends and insights that may be difficult to find manually (Owoc et al., 2021b). The application of AI in research can also speed up the process of data collection and analysis, allowing researchers to focus on interpretation and higher intellectual contributions. AI implementation strategies in higher education also need to pay attention to ethical and security aspects (Allen et al., 2022). The involvement of lecturers and students in developing AI ethics policies in higher education is key to ensuring that the use of this technology is in accordance with applicable academic values and ethical norms (Di Vaio et al., 2022). Ensuring data security is also a priority to protect the personal information of students and lecturers.
Developing human resource capacity in higher education is an implementation strategy that is no less important (Baker, 2016). Intensive training for faculty, administrative staff, and students will help increase understanding of the use of AI, minimize resistance to change, and ensure effective use of this technology.
Collaboration between universities and industrial partners is an additional strategy that can accelerate the implementation of artificial intelligence. Exchange of knowledge, resources and experience between educational institutions and industry can help accelerate innovation and overcome obstacles that may arise during the implementation process (Ahmad et al., 2022). Finally, regular evaluation and updates need to be integrated into the implementation strategy. Evaluating the performance of AI systems, responding to user feedback, and making necessary improvements and updates will help ensure that AI implementation in higher education remains relevant and effective over time. By designing and implementing a holistic artificial intelligence implementation strategy, higher education institutions can harness the potential of this technology to improve operational efficiency (Boranbayev et al., 2021), learning quality, and
contributions to research, creating a more innovative and adaptive educational environment.
2. Lecturer Performance Evaluation System in Higher Education
Lecturer performance evaluation systems in higher education have an important role in ensuring the quality of teaching, research and other academic contributions (Zawacki-Richter et al., 2019). Lecturer performance evaluation is not only a tool for assessing individual achievements, but also a basis for developing and improving the quality of higher education as a whole (Rashid, 2023). One of the main aspects in the lecturer performance evaluation system is teaching measurement. Lecturers are evaluated based on their ability to convey material, clarity of teaching, interaction with students, and effectiveness of learning methods. This evaluation may involve feedback from students, peer assessment, or direct observation. Teaching measurement is key because the student learning experience is greatly influenced by the quality of the lecturer's teaching. Apart from that, the research aspect is also a focus in the lecturer performance evaluation system. Lecturer contributions in scientific research, journal publications, applied research, and participation in research projects are important criteria in assessing academic performance. Productive research not only contributes to scientific understanding but can also improve the reputation of universities in the academic world.
Lecturer participation in community service activities and service to institutions is also an integral part of performance evaluation (Park & Han, 2018). Lecturers are evaluated based on their contribution in providing services to the community, writing policies, speaking at seminars or workshops, as well as their active role in social and cultural activities. Improving relations between universities and the community is the main goal in this evaluation aspect. Administrative and managerial aspects are also considered in the lecturer performance evaluation system (Ren, 2023). Efficiency in carrying out administrative tasks, leadership in academic organizations, and managerial abilities are part of the evaluation to ensure that lecturers can play an active role in managing the university as a whole. Apart from that, interdisciplinary collaboration and teamwork are also aspects that are increasingly being paid attention to in evaluating lecturer performance. Universities now value lecturers' contributions to interdisciplinary joint projects and cross-subject research. Lecturers who are able to work collaboratively can have a greater impact on innovation and solving complex problems.
The importance of sustainability and self-development is also reflected in the lecturer performance evaluation system. Lecturers are expected to continue to develop academic competencies, follow developments in science, and be involved in training or workshops that support their professional development. Performance evaluation is a means to encourage lecturers to continue to develop and make better contributions.
Although the lecturer performance evaluation system has many positive aspects, it can also face several challenges. One of the main challenges is subjective measurement and the potential for bias in assessment. Therefore, it is important to ensure that clear and
objective evaluation criteria are adopted to ensure fairness and accuracy. In facing these challenges, transparency and involvement of lecturers in the evaluation process is very necessary. An evaluation process that is open and involves lecturers in formulating evaluation criteria and standards can help minimize potential bias and increase trust in the evaluation system. The performance evaluation system for lecturers in higher education plays a crucial role in maintaining and improving the quality of higher education. A holistic evaluation, covering aspects of teaching, research, community service, administration, and collaboration, provides a comprehensive picture of lecturers' contributions in achieving the mission and vision of higher education. By continuing to develop and refine evaluation systems, universities can ensure that lecturers have the motivation and support to make their best contribution in achieving academic goals and providing a quality educational experience for students.
There are several previous research opinions. The first research according to Wahyudi & Sunarsi, (2021), with the research title Benefits of implementing knowledge management for lecturer performance during the Covid-19 pandemic. The results of his research stated that A general overview or more or less knowledge management helps the management of higher education institutions in adapting to the pandemic situation, especially in maintaining the performance of lecturers in carrying out education. This is proven, knowledge management encourages the birth of technology-based online education delivery systems, so that lecturers' performance in the field of education does not decline. The second research according to Putra & Pemayun, (2019), with the research title Information System Renstra For Measurement Of Lecturer Performance Using It Balanced Scorecard. The results of his research stated that the strategic information system for measuring lecturer performance using the Balanced Scorecard is very effective. The third research according to Maesaroh et al., (2022), with the research title Effectiveness of Implementing Business Intelligence Management in Industry 4.0. The results of his research stated that Industry 4.0 is the technology that companies need to promote innovation strategies and get a fast response in a dynamic market. It focuses primarily on interconnectivity, digital technologies, predictive analytics, and machine learning to revolutionize the way companies operate and develop.
RESEARCH METHODOLOGY
The method used in this research is using qualitative and quantitative approaches (G. R.
Bauer & Scheim, 2019). Data is obtained through literature studies, interviews with education and technology experts, and analysis of existing lecturer performance evaluation data (Allam & Dhunny, 2019). An AI system was developed to process and analyze evaluation data by utilizing machine learning algorithms to provide more accurate evaluation recommendations. This approach allows researchers to gain an in - depth understanding of the perceptions, expectations and challenges that may be faced by those involved in the implementation process. In addition, content analysis of policy documents, guidelines and meeting notes can provide insights into how strategies are
officially implemented. A focus on qualitative aspects will allow researchers to explore the social, political, and cultural dynamics that might influence the acceptance and adaptation of artificial intelligence in lecturer performance evaluation (Rofiah &
Bungin, 2021). On the other hand, quantitative research can be conducted by collecting numerical and statistical data to measure the impact of artificial intelligence implementation strategies. Online surveys or questionnaires can be distributed to lecturers, students, and administration to collect quantitative data related to the perception, effectiveness, and efficiency of the implemented evaluation system. By combining these two methods, the research can provide a more comprehensive analysis and provide deeper insights to stakeholders in higher education. It is important to note that both research methods have their own strengths and weaknesses. Qualitative research tends to be more in-depth and contextual, while quantitative research provides the power of quantifiable statistical analysis. Therefore, combining these two methods can provide a balanced and comprehensive picture of artificial intelligence implementation strategies in lecturer performance evaluation systems in higher education.
The steps in conducting research on the implementation of artificial intelligence in lecturer performance evaluation systems in higher education need to be carefully organized to ensure the success and validity of the research results. First, the research needs to start by detailing clear research objectives. The definition of objectives will help direct the focus of the research and guide the selection of the most appropriate research methods. Research objectives could involve a deeper understanding of lecturers' acceptance of AI technology, its impact on the effectiveness of performance evaluations, and students' perceptions of the use of artificial intelligence in the evaluation process. After establishing the research objectives, the next step is to design a conceptual framework. This conceptual framework may include a literature review on the implementation of AI in the context of lecturer performance evaluation. The literature review will help researchers understand the context, relevant theories, and previous findings that can serve as the basis for developing more specific hypotheses or research questions. Next, the selection of research methods is an important step. A combination of qualitative and quantitative methods can be an effective approach.
Qualitative methods such as in-depth interviews with lecturers, students, and administration can provide an in-depth understanding of their perceptions and experiences towards the implementation of artificial intelligence. On the other hand, surveys or questionnaires can be used to collect quantitative data that can be statistically analyzed to get an overview and measure the impact more broadly.
After designing the research method, the next step is data collection. This process involves applying the designed research methods, such as interviews, surveys, or observations. In the context of artificial intelligence implementation, data collection may involve interacting with evaluation systems that use AI technology, as well as documenting faculty and student responses to the experience of using the technology.
Data analysis is a crucial next step. Qualitative data can be analyzed using thematic
approaches or content analysis, while quantitative data requires statistical analysis methods such as regression or comparison tests. The results of the analysis will provide a deeper understanding of the impact and effectiveness of artificial intelligence implementation in the lecturer performance evaluation system. The final step is to organize the findings and draw conclusions. Research results need to be organized systematically and logically to answer the research questions that have been asked.
Conclusions may include practical and theoretical implications of the research findings, as well as suggestions for further development. Dissemination of research results can be done through scientific publications, conference presentations, or delivery to related parties.
RESULT AND DISCUSSION
Artificial Intelligence (AI) implementation relates to the process of applying and integrating artificial intelligence technology in a system or organization. AI is a branch of computer science that aims to develop computer systems or programs capable of performing tasks that require human intelligence, such as learning, adapting, and solving problems. Basically, the implementation of artificial intelligence involves utilizing algorithms and mathematical models to teach computers how to perform certain tasks without involving explicit programming. This process includes data collection and analysis, model training, and integration of artificial intelligence solutions into appropriate environments. The implementation of artificial intelligence can occur in a variety of contexts, including industry, healthcare, education, finance, and more. For example, in the manufacturing industry, artificial intelligence can be applied to improve efficiency in the production chain through process optimization, predictive maintenance, and automated quality control. In healthcare, the implementation of artificial intelligence can help in disease diagnosis, treatment planning, and drug development. The main goal of artificial intelligence implementation is to improve the performance and efficiency of a system or organization. Some specific goals involve improved prediction and analysis, automation of complex tasks, smarter decision making based on data, and improved user experience.
The Lecturer Performance Evaluation System in higher education has a crucial role in maintaining and improving the quality of higher education. This evaluation not only covers teaching aspects, but also involves research, community service, and other academic contributions. The Lecturer Performance Evaluation System is a mechanism designed to measure, assess, and provide feedback on lecturer performance in various dimensions. This includes teaching quality, research productivity, participation in community service activities, and contributions to academic and institutional life. This evaluation is conducted periodically to provide an accurate picture of lecturers' achievements and contributions to the mission and vision of the college. The main objective of the Lecturer Performance Evaluation System is to improve the overall quality of higher education. The evaluation aims to provide constructive feedback to lecturers, motivating them to continuously improve the quality of teaching, research, and service to the community. By involving lecturers in the evaluation process, this goal is expected to create a dynamic and innovative academic environment. Lecturer
performance evaluation can assist in human resource management and career development.
By systematically assessing lecturer performance, higher education institutions can identify lecturers' strengths and weaknesses, and plan career development accordingly. This involves providing opportunities and support to improve skills, supporting promotions, and recognizing outstanding contributions.
The implementation of artificial intelligence (AI) in lecturer performance evaluation systems in higher education brings a number of significant benefits, transforming the traditional paradigm of evaluation into a more efficient, objective and adaptive approach.
These benefits involve various aspects, ranging from increased efficiency to the development of the overall quality of higher education. First of all, the implementation of artificial intelligence can improve efficiency in the lecturer performance evaluation process. AI systems can automatically collect, process, and analyze lecturer performance data with speed and accuracy far beyond human capabilities. As such, the time required to complete the evaluation can be significantly reduced, allowing resources and time to be allocated to other activities that support academic development. Artificial intelligence also brings an aspect of objectivity to lecturer performance evaluation. With carefully orchestrated algorithms, evaluation decisions can be based on consistently predefined data and criteria. This reduces the possibility of personal bias or subjectivity in lecturer performance assessment, creating a fairer and more accurate environment. Lecturers can get objective feedback based on pre-set criteria, providing a solid foundation for academic career development. The implementation of artificial intelligence also opens up opportunities for personalization in lecturer professional development. AI systems can analyze individual performance patterns, identify strengths and weaknesses, and provide specific recommendations for continuous development. Lecturers can receive advice and resources tailored to their needs, helping them to achieve their goals.
Another benefit is the increased accuracy in measuring the impact of lecturers' teaching on students. By utilizing artificial intelligence technology, lecturer performance evaluations can focus more on student learning outcomes and positive contributions to their academic development. The system can analyze evaluation data and create a more comprehensive picture of how lecturers influence student understanding and achievement. In addition, the implementation of artificial intelligence can improve understanding of students' needs and preferences. Through analyzing data from lecturer-student interactions, AI systems can identify trends, tendencies, and specific needs of students. With this information, lecturers can approach teaching more adaptively, providing a more personalized and relevant learning experience. The benefits in the context of faculty research development are also evident. AI systems can help detect research opportunities, analyze literature trends, and provide relevant topic recommendations. This not only speeds up the process of identifying potential research, but also ensures that lecturers remain at the forefront of knowledge and innovation in their field. In addition, the implementation of artificial intelligence can create a more collaborative work environment. Lecturers can use AI platforms to share ideas, facilitate academic discussions, and work together on research projects. This opens up wider collaboration opportunities, enabling the exchange of knowledge and experience among the academic community. Social and cultural benefits also arise from the implementation of artificial
intelligence in lecturer performance evaluation. The academic community can access more transparent information on lecturer performance, increasing accountability and public trust.
Successful lecturers can be recognized more appropriately and motivate them to continue contributing to the development of knowledge.
Table 1: Implementation of Artificial Intelligence in Lecturer Performance Evaluation System in Higher Education
No Implementation Aspect
Findings Analysis of findings
1 Lecturer Admission
Most lecturers showed positive acceptance of the implementation of artificial intelligence in the performance evaluation system.
This positive reception can be caused by lecturers' understanding of the potential to increase efficiency and objectivity of evaluation by utilizing artificial intelligence technology. Therefore, effective communication about the benefits of technology needs to be strengthened to build lecturer trust and acceptance.
2 Resource Availability
Most universities have limited technological resources and IT expertise to implement artificial intelligence.
Limited resource
availability can be a significant barrier to adopting artificial intelligence. Therefore, the strategy needs to focus on intensive training for lecturers and administrative staff as well as investment in supporting technological infrastructure.
3 Availability of Quality Data
The data needed to train artificial intelligence models is limited and does not always accurately
reflect lecturer
performance.
The availability of quality data is a critical factor in the successful implementation of artificial intelligence.
Higher education
institutions need to ensure that the data used is representative and covers
various aspects of lecturer performance.
4 Transparency and Ethics
There are concerns regarding algorithm transparency and fairness in the performance evaluation process.
Lecturers and students feel the need to understand how artificial intelligence makes decisions. Therefore, transparency and active involvement of parties in the formulation of ethics policies are essential to ensure fairness and acceptance of this technology.
5 Impact on
Teaching and Research
Hasil implementasi kecerdasan buatan belum secara signifikan mempengaruhi
peningkatan kualitas pengajaran dan penelitian dosen.
Despite the great potential, the results show that the implementation of artificial intelligence has not fully achieved the expected positive impact. Therefore, strategies need to focus on
developing more
sophisticated and integrated models to maximize the contribution of artificial intelligence in supporting improving the quality of lecturers' teaching and research.
Based on the table above, it can be understood that lecturer acceptance of artificial intelligence implementation is a key element of success. A good understanding of the benefits of this technology can increase lecturer acceptance and engagement in the evaluation process.
It is important to establish effective communication, provide training, and facilitate discussion forums to respond to lecturers' concerns and questions. Furthermore, the limited technology resources and IT expertise in higher education need to be addressed through appropriate investment. Intensive training for lecturers and administrative staff is an important step so that they can understand and use artificial intelligence technology effectively. Universities also need to collaborate with industry or research institutions to support the development of adequate technology infrastructure. The availability of quality data is a critical factor.
Universities need to make efforts to improve the collection and maintenance of accurate and relevant data. Collaboration with institutions or industries that can provide quality data can be an effective strategy to ensure the successful implementation of artificial intelligence.
Next is transparency and ethics. Concerns related to transparency and ethics emphasize the importance of developing policies that are transparent and participatory.
Universities need to involve lecturers, students and administration in formulating ethical policies that uphold academic values and fairness in lecturer performance evaluation. The impact on teaching and research should also be considered. Despite the great potential of artificial intelligence implementation in improving the quality of lecturers' teaching and research, the results show that the positive effects have not been fully achieved. It is necessary to develop a more sophisticated and integrated model, as well as continuous monitoring of its impact to maximize the contribution of artificial intelligence in supporting academic progress in higher education. Through this approach, artificial intelligence implementation strategies can be continuously developed and adapted to the dynamics and needs of higher education.
By paying attention to the findings and analysis, universities can guide a more effective and supportive implementation of artificial intelligence in improving lecturers' performance evaluation and the overall quality of higher education.
CONCLUSION
Based on the results and discussion above, it can be concluded that the artificial intelligence implementation strategy can be successfully implemented in the lecturer performance evaluation system in higher education. With increased objectivity, accuracy and efficiency, this system makes a positive contribution to human resource management in the academic environment. Apart from that, it is also to encourage further implementation of artificial intelligence (AI) technology in evaluating lecturer performance to improve the quality of higher education. The implementation of artificial intelligence in lecturer performance evaluation systems in higher education promises various significant benefits. This strategy not only speeds up the evaluation process, but also increases accuracy and objectivity in assessing lecturer performance. By leveraging algorithms and artificial intelligence models, higher education institutions can optimize data use, provide more detailed feedback, and provide a more informed basis for decisions. The implementation of artificial intelligence also opens the door to personalization in lecturers' professional development. Through analysis of individual data, the system can identify the unique needs and potential of each lecturer, providing tailored recommendations for more effective career development. This creates an environment that supports sustainable professional growth and is relevant to the dynamic changing demands in the world of education. The successful implementation of artificial intelligence in lecturer performance evaluation also depends on the active involvement and acceptance of all stakeholders. Faculty, students and administrative staff need to be empowered and given adequate understanding of the potential and limitations of this technology. Stakeholders who support the implementation process will help create an environment that supports positive development and acceptance of innovation in the lecturer performance evaluation system.
ACKNOWLEDGEMENT
Previously, the researcher would like to thank those who have helped and allowed the researcher to research the research entitled Strategy for Implementing Artificial Intelligence in Lecturer Performance Evaluation Systems in Higher Education. Hopefully the research conducted by this researcher can become a reference for future researchers.
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