This paper presents a comprehensive review of the integration of Multi-Intelligent Agents (MIA) and Generative AI (GenAI) in learner assessment. Through a systematic bibliometric analysis of literature from 2019 to 2024, we identify key research trends, methodologies, and applications in educational contexts. Our findings reveal that MIA excels in orchestrating adaptive and collaborative assessments (34.6% of research), while GenAI demonstrates significant potential in higher education (50% of research) for content generation and personalized feedback. The combined approach remains underexplored (14.6% of studies) but shows promise for developing more effective assessment frameworks. This study contributes to the field by mapping the evolution of MIA and GenAI in assessment, identifying current gaps in empirical validation, and proposing a hybrid model that enhances both automation and personalization. It offers a critical perspective on practical challenges and ethical concerns, setting a structured research agenda for future explorations.

As the demand for more effective and efficient eLearning assessment systems continues to rise, there is a growing need for innovative approaches. This paper introduces the Intelligent Collaborative Assessment Framework (ICAF), which is a novel ontology-based framework that integrates Artificial Intelligence (AI) and collaborative tools to enhance assessment activities in cyberspace eLearning environments. Our research approach involved developing a specific ontology to capture essential concepts related to collaborative eLearning assessment, guiding the creation of the ICAF. The architecture of the ICAF consists of interconnected components that work in synergy to support assessment activities in cyberspace eLearning environments. One of its key features is the integration of collaborative learning techniques, which fosters a more engaging and effective learning experience. Additionally, the framework incorporates learning analytics and machine learning algorithms to analyze group learning patterns and deliver precise and valuable feedback. By presenting the viability and potential benefits of the ICAF, we demonstrate how the ICAF has the potential to significantly enhance assessment practices and facilitate collaborative learning in cyberspace eLearning environments.

Assessment is a critical component of any teaching-learning process. It is the only way to gain a better understanding of the knowledge and abilities of the learners, especially in a collaborative learning environment. The learner must pay special attention to interactions and real-world context when selecting collaborative situations and activities. As a result, it is critical to ensure that assessment methods and techniques are adapted so that the learning process is better suited to each group of learners, with their unique learning style, background, needs, previous experiences, and most importantly, context. This paper addressed two major issues in the development of a personalized formative assessment system that will be used to assess the knowledge, skills, and competencies of individual and group learners in an online collaborative learning environment. One approach is to design an adaptive assessment path that helps learners achieve specific learning goals based on previous learner performance, level, and context. The other is to make a personalized recommendation using a Context-Aware Recommender System (CARS), which monitors group formation while performing collaborative assessment activities. To formalize and well define our proposed system, we suggest a semantic web approach using ontologies and eLearning standards, CMI5 to describe the proposed assessment path, and IEEE PAPI for the learner model, all of which allow data reusability and interoperability.