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Artificial Intelligence in Formative and Shared Assessment in Higher Education: History
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Contributor: José Luis Aparicio-Herguedas , Miriam Molina-Soria , Teresa Fuentes-Nieto , Víctor M. López-Pastor

The use of Artificial Intelligence (AI) in Formative and Shared Assessment (F&SA) processes refers to the application of AI-based technologies to support formative and continuous assessment in Higher Education (HE). F&SA systems involve the ongoing monitoring of students’ learning, the provision of feedback that enables them to regulate and improve their performance, and the collection of information that informs the continuous improvement of teaching practice. In this context, AI can serve a dual purpose: when orientated towards students, it enhances learning outcomes; when directed at educators, it supports the development of their pedagogical expertise through tools designed to assist in the creation of assessment instruments, the generation of automated feedback, the analysis of learning data, and the design of simulation environments that foster the development of professional competencies. The integration of AI into F&SA practices holds considerable potential to transform traditional assessment approaches by enabling more personalised, adaptive, and timely feedback for both students and educators. In this shared assessment framework, students may likewise draw on AI applications to support specific dimensions of their learning, including academic writing, knowledge organisation, and the generation of educational content, thereby becoming active participants in their own assessment processes. However, the incorporation of AI into F&SA also requires careful consideration of the pedagogical, ethical, and institutional challenges it entails, particularly those related to academic integrity, cognitive offloading, and the responsible use of AI tools. It is therefore essential to promote AI literacy in HE among both faculty members and students, fostering a critical and informed engagement with these technologies that ensures the pedagogical relationship, along with the shared, formative nature of assessment, remains at the core of meaningful learning processes.

  • formative assessment
  • artificial intelligence
  • higher education
Overcoming traditional assessment models may be one of the greatest challenges that university faculty must face throughout their professional careers. The influence of many years of a traditional assessment culture, centred on examinations and grading, has been deeply ingrained. Some authors [1][2] have addressed the challenge of transitioning from an examination culture to an assessment culture, one that is far more formative and concerned with student learning. Traditional assessment places the entire evaluative weight on the final examination, on a last test or academic product from which the final grade is determined. The culture of formative assessment (FA) rests on the process of improving learning through procedures and protocols that foster feedback and learning. It reserves the exercise of grading for specific moments so that the possibility of improvement following feedback is genuinely effective [3].
With the implementation of the European Higher Education Area (EHEA), certain changes occurred that affected assessment; among them, the role of students (as the centre of the formative process) and that of knowledge (competencies to be developed through the subjects included in degree programmes). A shift also took place towards a more dialogic learning model. All of this entailed a revision of both didactic methodology and assessment practices.
Nearly two decades later, FA systems appear to be slowly beginning to be adopted in HE [4][5][6][7][8], as they perform reasonably well in enabling students to learn more effectively and educators to teach more successfully; specifically in terms of the quality of the work produced by students throughout the formative process, the improvement of teaching practices and the enhancement of teaching–learning processes—both in the moment and retrospectively [9][10].

Digital Transformation of Assessment in Higher Education

The specialised literature [11][12][13][14][15] has consistently shown that FA systems can benefit from technology through processes of continuous feedback or virtual classrooms and platforms. For instance, in the case of the former, through the application of Audience Response Systems (ARS) and questionnaires that facilitate classroom interaction and provide accessible and frequent feedback. Faculty members can pose questions about the content under study and students respond in real time via their mobile devices connected to software that collects and displays their answers. In this way, an interaction is established mediated by questions, moments of dialogic discussion and a process of constant feedback. In the case of the latter, Learning Management Systems (LMS), or virtual classrooms, allow for the creation and assignment of tasks, the administration of online quizzes and work through e-portfolios or discussion forums. The process is monitored, evidence of learning is collected and processes of reflection and self-regulation are supported [16][17]. Their inclusion promotes a model of digital FA that integrates three pedagogical strategies: (1) discussion and questioning, (2) feedback ensured throughout the entire formative process, and (3) self-assessment and peer assessment. It also contributes three technological functionalities: the submission and visualisation of information, the processing and analysis of learning outcomes and interaction within digital environments.
Furthermore, certain advanced technological tools applied to FA systems are capable of interpreting responses and generating automatic feedback [18][19]. Tools based on text mining, automated response analysis and continuous monitoring can provide immediate feedback and support the personalised tracking of student learning. Authors have sought to address some of the implementation challenges of FA, such as managing large student groups or reviewing lengthy or complex pieces of work; these may serve as illustrative examples of a certain transition from the digitalisation of assessment toward the current use of AI in FA.

The Emergence of Artificial Intelligence as an Agent of Change in Assessment Processes

AI emerges as a set of technologies whose computational systems possess the capacity to imitate cognitive processes inherent to human beings, such as pattern recognition, the consideration of alternatives and decision-making. These systems are capable of analysing large volumes of data through algorithms, reasoning about them, self-correcting and learning through continuous interaction with people [20][21][22]. Its incorporation into higher education, which represents a continuation of the advances in technology-assisted assessment, is increasingly prevalent and constitutes one of the most active emerging milestones in educational technology [20]. The capacity for generating original content, text, images, audio and video, through instructions formulated in natural language is characteristic of an evolution of AI known as “generative” AI (of which “ChatGPT” and “Gemini” are representative examples), and its effects are having a significant impact on HE [23][24][25]. The positive impact is associated with the transformation of formative and assessment processes. The risks, on the other hand, are related to its indiscriminate use (as a simple shortcut for completing academic tasks to be submitted), the absence of reliability guarantees regarding the information generated and the potential violation of academic integrity [26][27][28].
This positive impact is documented in a number of studies, such as that conducted by The Digital Education Council on models of AI integration in higher education institutions [29]. This study reveals a model of AI inclusion as a support tool for analysing information, generating formative content under faculty supervision and designing instructional materials [30]. Other related studies [21][31][32] describe its incorporation into assessment processes in the form of virtual assistants and intelligent tutoring systems.

Pedagogical Relevance of Formative Assessment

FA is essential for improving student learning outcomes, but it should equally be oriented toward enhancing both the learning processes and teaching competencies of faculty members, as well as the teaching–learning processes carried out in the classroom. In this regard, the model known as Formative and Shared Assessment (F&SA) is designed to fulfil these three purposes in a complementary manner. This model combines two complementary concepts in an attempt to generate assessment of the highest educational quality [9]. The concept of “formative assessment” indicates that the primary purpose of assessment is the improvement of student learning processes in HE, but it should also serve to enable faculty members to progressively enhance their teaching competencies and to inform improvement decisions regarding the teaching processes developed by both students and faculty in HE [2][9]. The concept of “shared assessment” refers to student participation in assessment processes through different techniques—self-assessment, peer-assessment and co-assessment—and their educational advantages in terms of learning and the development of professional and personal competencies [2][9].
Within the F&SA model, the roles of feedback and feedforward are fundamental, embedded within continuous assessment and learning processes in which assessment is fully integrated with the completion of learning activities, thereby generating ongoing cycles of reflection and improvement [2][9][33][34]. Reviews of its advantages and limitations can be found in recent studies [33][34][35].

 

This entry is adapted from the peer-reviewed paper 10.3390/encyclopedia6070158

References

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