A paradigm shift in the assessment of learning

(The opinions expressed in this article are those of the author and do not necessarily reflect those of the al-fais media).

Artificial intelligence (AI) leads to a paradigm shift in teaching and learning at all levels. As with any change, this brings both opportunities and challenges, in particular with regard to educational evaluation.

The evaluation is at the heart of any teaching and learning process. Traditionally, the evaluations have been designed to assess students' performance through summative approaches such as standardized tests and final exams. However, the accent was growing on formative evaluationwhere the objective is to integrate evaluation tasks into learning activities. This type of assessment is also called learning assessment, and its objective is to support students' learning throughout the process rather than simply measuring their success.

With AI tools now available, it is imperative that we repent of evaluations to make them more significant, personalized and aligned on contemporary educational needs.

The tools fueled by AI can automate the classification, provide real -time comments and generate adaptive assessments according to students' progress. These technologies can improve evaluation practices by making them more effective and adapted to individual learning needs. However, they also raise concerns about Academic integrityTHE fasonization education, and the risk that students become too dependent on the ai-generated content Rather than engaging in in -depth learning.

One of the critical challenges faced by educators is now how to take up this dilemma. In other words, how to use the tools available now to make teaching and learning more significant for stakeholders and ensure responsible and ethical use that promotes the student agency.

To meet this challenge, we must find an objective through which we can define and design contemporary educational assessments. There are two theoretical executives or objectives that can help educators define and design evaluation tasks: the Proximal development area (ZPD) and Apprenticeship assessment (AFL).

Developed by Lev Vygotsky, the proximal development area refers to the gap between what learners can do independently and what they can achieve with advice from a more competent or more skillful “other”, such as parents, teachers and peers. AI can now act as “more competent”, supporting students while they resolve challenges beyond their immediate capacity. AI tutors, for example, can scaffold learning by providing advice, comments and personalized instructions that align with the current level of understanding of students. When used effectively, AI tools can help fill learning gaps and encourage cognitive development. If they are poorly used, however, they can lead to superficial learning where students bypass the control necessary for the verification of facts and the understanding and interpretation of the content generated by AI.

The assessment of learning, on the other hand, emphasizes the use of evaluation not only as a tool of judgment but as a means of improving learning. AI can facilitate this approach by providing continuous feedback, identifying false ideas and adapting evaluation tasks to meet the evolutionary needs of students. Rather than relying on unique exams with high stakes, educators can integrate AI -focused assessments in the learning process to promote self -regulation and metacognition. Students can engage with platforms fueled by AI which offer interactive quizs, real -time analyzes and monitoring of progress, helping them to reflect on their learning journey.

Balance the advantages and challenges

While AI offers promising progress in the evaluation, educators must carefully approach its integration. AI's ability to generate responses and automate tasks raises concerns about the authenticity of students' work. To counter this, educators should conceive of assessments that require critical thinking, creativity and personal reflection – tasks that AI cannot easily reproduce. Open projects, problem solving tasks and collaborative work can encourage students to get involved with content significantly rather than simply reproducing responses generated by AI.

The key to the apprenticeship assessment approach is a transition from evaluation tasks focused on processes. An example would be evaluation tasks based on projects where students can be assigned to the design of small -scale projects and to use AI for brainstorming, contour generation and content generation. However, throughout the process, students must assume the responsibility to verify the results of AI tools, to criticize them critically, to synthesize relevant information and to create reports. In this way, not only do they learn to use AI tools, but also to use these tools responsible for improving their critical thinking and agency.

As AI becomes a basic food in education, promoting Itaph is essential. The definition and design of evaluation tasks that require AI tools will promote literacy of students' AI. Students and educators must, however, understand ethics implications learning assisted by AI, including issues of biasConfidentiality of data and academic integrity. Clear directives must be established at the moment and how AI can be used in evaluation tasks to ensure that it serves as a learning help rather than shortcut. Educators can thus develop guidelines for each evaluation task to help students understand how it is possible to use AI tools ethically and responsible.

One of the critical messages of all educational stakeholders is that AI should complete, rather than replacing, the role of the educator and the learner. Although AI can provide unprecedented support to learners and precious information on the progress of student learning, the human agency and judgment must remain crucial to interpret the nuanced aspects of learning, such as criticality, creativity and emotional commitment. Educators must actively design and moderate the assessments fueled by AI to align with the educational objectives.

Ahead

The AI ​​transforms educational assessment, offering new opportunities for a personalized, efficient and formative assessment. Its integration must nevertheless be guided by sound educational principles. By taking advantage of executives such as the proximal development zone (ZPD) and the assessment of learning (AFL), educators can use the potential for improving AI rather than undermining learning. The key lies in the abolition of a balance between the use of AI as a support tool while maintaining man -centered educational practices that promote in -depth learning and critical commitment.

While AI continues to evolve, our responsibility as a Educators is to adapt in a reflected way, ensuring that evaluation remains a significant and ethical part of the learning process.

A. Mehdi Riazi Is a professor at Hamad Bin Khalifa at the University (HBKU) College of Humanities and Social Sciences. This play was submitted by the HBKU communications department on behalf of its author. The thoughts and opinions expressed are the own and do not necessarily reflect an official university position.

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