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Vídeo em português do Brasil.

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Moluscos para crianças - Animais invertebrados - Ciências

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Atividade 2

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Goraz

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Atividade 1

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Tudo Sobre Arraias | Raias | Peixe Batóide | Características | Hábitat | Reprodução

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Aqui 🤬 a 1.ª tentativa de um vídeo.

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Como fazer Peixe Articulado de Papel - artesanato fácil para crianças

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Moving Paper Shark | Paper Crafts for Kids

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Jamanta - construção com folha colorida A4

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Jamanta - construção com folha colorida A4

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Moving Paper Fish Stingray | Paper Craft for Kids

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Realizado na sala - atividade que fizemos no + mar.

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A pedido dos meninos aqui 🤬 a descrição da atividade que fizemos no + mar da última semana de aulas.

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1º Pintar o desenho.
2º Instalar a aplicação Spectacular no tlm ou tablet.
3º ler o QR code do desenho .

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Terminar o projeto até 6ª feira.

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Constrói um animal marinho com material reciclado. (Ao longo da semana)

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Quizlet é uma excelente ferramenta de IA que pode ajudar os professores a criar flashcards interativos e 🤬 para seus alunos. Veja como funciona.

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Os tutores virtuais baseados em IA, como o chatbot da Duolingo, oferecem assistência individualizada aos alunos. Este artigo explica os prós e contras.

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Monster Quest | AI tutor The No.1 Homework Finishing Free App

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Monster Quest : AI tutor is a personal tutoring app that resolves questions on the spot by simply taking a photo of the problem or explanation and sending it to Discord. The Monster Quest : AI tutor teaches in a conversational style, pinpointing even the hard-to-understand points that are not clear from textbook or workbook explanations alone.

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Descubra como ferramentas de IA podem ser usadas para criar experimentos de laboratório simulados e aulas de ciências interativas.

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The artificial intelligence (AI) revolution in education has arrived. Not a day goes by now when AI is not mentioned in relation to how it may be integrated in teaching and learning across the education sector, including in higher education.

Like many technological innovations, the excitement is building up about how AI tools might help enhance education. Universities are developing specialised courses to enhance students' knowledge about AI and its integration in professional settings.

As is often the case with new technologies, it is possible that the ongoing excitement may lead to mindless use of AI tools as opposed to meaningful engagement with understanding what the tools are enabling professionals to do.

For science education, it is worth pausing and reflecting on how AI is influencing scientific research itself and what recent developments about the use of AI in science imply for science education in universities.

AI in science

Currently, we are witnessing an explosion of AI use in basic scientific research, from chemistry to pathology.

Scientists are relying on AI tools in designing experiments, generating hypotheses and interpreting data. Practically all aspects of the knowledge generation practices of science, from writing of manuscripts to modelling of data, are being influenced by AI.

When one is engaged in using a particular AI platform for a very specific purpose, for example, predicting the molecular structure of a protein, it is easy to lose sight of the 'big' picture about how such AI tools are changing science.

It is one thing to use a tool to investigate or solve a problem and another to understand how the tool helps produce scientific knowledge and indeed how it may be transforming the entire enterprise of science.

The latter broad perspective requires a more reflective stance where one steps back and looks at what the tool enables scientists to do or not to do, and indeed what its ramifications are more broadly for science, be that in terms of the social, ethical or professional implications.

A time for reflection

Why is such a reflective perspective on the impact of AI on science important for university science education? First, we need to acknowledge that university students are learners and that their learning needs to be supported. There is a vast amount of research in science education that highlights students' difficulties with scientific concepts as well as their understanding of how science works.

Even graduates of top universities demonstrate significant misconceptions about some rather basic science concepts, such as photosynthesis, that are taught at lower secondary school science lessons. For decades now, key professional organisations have recognised such difficulties and the significance of the curriculum and teaching in addressing them.

It is imperative, then, that when university science education introduces students to AI that it includes a component where they not only use the tools to solve scientific problems but also have the space to 'think' about AI and its impact on science.

Encouraging broader conversations about AI

How can such a learning objective about AI in university education be achieved? There may be different strategies, some of which could include the following.

First, the curriculum needs to have an explicit component that deals with AI and its impact on science. Such a curriculum inherently calls for an interdisciplinary and cross-curricular approach where, apart from the physical and natural scientists, other key stakeholders such as computer scientists, ethicists, philosophers and social scientists can provide input to enhance science students' understanding of key issues.

In fact, cross-departmental interactions with students outside of science might prove useful for learning from peers. After all, doing research is being transformed in many domains, for example, the social sciences, and a broader conversation about AI will help its positioning within science.

Second, in the true spirit of science, the approach to innovation in the use of AI in university science education needs to prioritise evidence and not ideology or hype about educational tools.

A robust educational research agenda needs to be put in place in order to support an evidence-based approach to improving students' understanding of and engagement with AI in science.

It is often the case that ' Education with a big E ' is misunderstood, undermined or altogether ignored. 'Education with a big E', as previously discussed, is about 'educational research', an endeavour that prioritises evidence.

It is not education in the sense of 'teaching' or 'training'. Since many of us have experienced schooling, it is easy to speculate about what should be or shouldn't be done in education because our hunches lead us to believe that they will be effective.

However, opinions about education are just that: opinions, which may or may not correspond to what actually happens in practice. A research-based approach will be more fruitful and robust in justifying effective strategies for the teaching and learning of science in the context of AI.

Finally, the fast pace of developments in the use of AI in science requires a serious consideration of what is being taught in secondary science education to ensure that students come to university with some basic understanding of how AI is influencing science.

Students need to be exposed to the AI agenda earlier in their education so that a thoughtful transition can happen from secondary to tertiary education. Much responsibility falls on the shoulders of school and university leaders when it comes to developing an ecosystem of education that supports students, teachers and instructors in enacting a coherent vision.

In short, AI is revolutionising not only educational tools but also how science itself is done. We need to ensure that we apply educational intelligence to dealing with its ramifications for raising the future generations of scientists.

Sibel Erduran is professor of science education and director for research in the department of education at the University of Oxford in the United Kingdom. She is also editor-in-chief of Science & Education and an editor of the International Journal of Science Education.

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How artificial intelligence is changing science education

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Artificial intelligence is revolutionising not only educational tools but also how science itself is done. Universities and researchers need to ensure...

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Ferramentas como o Rosetta Stone e o Google Translate estão revolucionando o ensino de línguas. Saiba mais sobre suas funcionalidades e eficácia.

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Chat about any topic and get instant feedback from the world’s most advanced AI language model, powerd by ChatGPT technology.

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O meu nome é Patrícia Breites, sou do grupo 110, do QA de Cister. No presente ano letivo iniciei funções como representante para a Autonomia e Flexibilidade Curricular, no CFAE de Alcobaça e Nazaré. Sou apaixonada pelo currículo, projetos, tecnologias e pela Literacia do Oceano, tema em que me encontro a desenvolver um doutoramento.

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Avaliando o Desempenho dos Alunos com a IA

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Avaliando o Desempenho dos Alunos com a IA

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A IA pode ser usada para desenvolver sistemas de avaliação contínua que monitoram o progresso dos alunos e personalizam a aprendizagem.

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Department of Statistics, Mathematics and Computer Sciences, Miguel Hernández University, 03202 Elche, Spain

Department of Didactic and School Organization, University of Murcia, 30100 Murcia, Spain

Department of General Didactic and Specific Didactic, University of Alicante, 03690 Alicante, Spain

Author to whom correspondence should be addressed.

1. Introduction

Advances in digital technologies and computer sciences are leading us towards a technological society where progressively machines are designed and developed to meet human needs while simultaneously becoming smarter. There is a general consensus [ 1, 2, 3, 4, 5] that Artificial Intelligence (AI) will be one of the most valuable technologies for the coming years, in conjunction with others such as robotics, virtual reality, 3D printing or networks.

As Curugullo [ 6] remarks, there is not a universal definition of AI, but we can easily agree that it describes the integration of artificial (not a natural process, but one induced by machines) and intelligence (skills of learning, to extract concepts from data and to handle uncertainty in complex situations). Finally, the author concludes that AI is "an artifact able to acquire information on the surrounding environment and make sense of it, in order to act rationally and autonomously even in uncertain situations" (p. 3).

AI is applied to different contexts, so we can see applications to architecture in smart buildings and smart cities [ 6], smart mobility [ 9], medicine [ 10, 11, 12], smart industry (the 4th industrial revolution, as some authors [ 13] remark) and also smart education or smart classrooms [ 14].

In relation to ethical uses of AI, UNESCO [ 1] is working on the elaboration of a global framework to guide uses and applications of AI in order to ensure the ethical use of these emerging technologies. We must consider the multiple advantages but, simultaneously, we must anticipate risks, malicious uses, and divides to guarantee human rights and dignity.

So, it is evident that not only is AI a relevant topic of research and innovation in education, but in general, for the future of our society. We are going to explore the real state of their applications in student assessment, because it is one of the fields with more relevant and innovative AI applications in education, as we are going to prove. This paper is organized in five sections. The next two sections are focused on the theoretical framework, so we introduce the educational approach to AI and the uses of AI for assessment. Next, we explain our method to develop the systematic review based on PRISMA model (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). Finally, we present the main results to sustain the discussion, conclusions, and future trends. The main contributions of this paper include:

Current revision of the state of the art.

The most recent applications of AI to assess students.

Based on our analysis, we suggest the main ways to improve education using AI and make some predictions on the future of this field of research.

2. Educational Applications of AI

As we have said above, one of the most relevant applications of AI is in the field of education. We refer not only to face-to-face education and smart learning environments, but also, and principally, in e-learning, making real the automatic and personalized learning processes based on adaptive learning, machine learning, ontologies, semantic technologies, natural language processing or deep learning. The origin of automatic learning processes goes back to, and is based on B.F. Skinner's teaching machine and programed learning [ 15], but we have observed a big evolution in them ever since.

In 2019, a lot of experts in AI attended the Beijing Conference and they agreed upon the "Beijing Consensus on AI and Education" [ 16]. In this document, they highlight the relevance of promoting AI in education, as in the Sustainable Development Goal number 4 set by UNESCO [ 16]. The main actions to promote are the following: to plan educational policies; to promote uses for education management, to empower teachers and learners; to promote values and skills for life and work; to offer lifelong learning opportunities and break the barriers in the digital world; to encourage equitable and inclusive uses, including gender equality; ethical and transparent uses; to research, evaluate and support innovation.

There are some interesting real experiences around AI, and the Horizon Report [ 17] compiles some of them, such as Watson Tutor by IBM, which has been tested with teachers and students in Texas or with Canvas LMS; another experience is from UC San Diego, the DSMLP (Data Science/Machine Learning Platform), which uses machine learning to provide access to resources and student projects; the third example is Boost, a mobile app integrated with Canvas LMS, a personal assistant for online learning; or finally, the intelligent tool Edulai, for assessing and developing soft skills. These experts [ 17] consider that "despite ethical concerns, the higher education sector of AI applications related to teaching and learning is projected to grow significantly" (p. 27).

Another relevant report [ 16] analyzes three areas: learning with AI (the use of these tools in education), learning about AI (how to use these tools) and preparing for AI (understanding their potential to change our lives). In relation to the use of these tools in education, we can find diverse experiences, but overall, these are related to tutoring or assessment.

2.1. AI for Tutoring

We can find interesting studies about intelligent tutoring systems [ 13, 18], which report that its development goes hand in hand with human natural language processing systems and learning analytics. All these systems are based on the idea of efficient mechanisms to provide feedback with enough quality to complement the teacher's action and substitute it in some cases. Considering the diversity of complex tasks that students can develop, it is a relevant advance to understand that these systems provide personalized answers. Ocaña et al. [ 13] remark that AI will mainly impact the process of personalized education because of the automated assistance, especially in the context of virtual interaction, and they include some very well-known examples such as Duolingo, which is the visible demonstration of the accessibility of applications based on the interaction between machines and humans, relying on AI principles.

Narciss et al. [ 19] remark that personalized tutoring feedback is one of the fields with more educational applicability, related to computer-based technologies. These authors studied a web-based intelligent learning environment used in mathematics to correct tasks and trace students' progress. They concluded that are some differences in feedback efficiency related to gender, because firstly, females take more advantage from tutoring feedback conditions (especially when feedback is based on conceptual hints). Secondly, girls improved their perceived competence more than boys, and finally, boys showed an increase in intrinsic motivation.

Jani et al. [ 20] demonstrate the usefulness of IA in both feedback and assessment as well as for formative evaluation, by using machine learning and checklists. Its research showed that automatic answers were good strategies to track students' progress and to identify areas to improve clinical practices.

The experience of Santos and Boticario [ 21] is of special interest because it utilizes AI to support collaborative learning experiences. They propose a Collaborative Logical Framework (CLF) based on AI to promote interaction, discussion, and collaboration as educational learning strategies. They also use this intelligent support to monitor students' behavior, reducing the workload in teachers. It is an adaptive guidance system developed for an e-learning platform (dotLRN) that can help the control and management of students' collaboration.

Ocaña-Fernández et al. [ 13] studied AI applications in higher education, and they conclude that intelligent tutors "have offered supportive assistance on several topics from their modest origins; topics such as training in geography, circuits, medical diagnosis, computing and programing, genetics and chemistry" (p. 563) and that they can be basic tools to improve the presumably ubiquitous online learning in the future. Some of the applications for providing feedback and tutoring are also thought for assessment, for instance Samarakou et al. [ 22] or Saplacan et al. [ 23].

2.2. AI for Educational Assessment

We can find interesting applications of AI linked to feedback. Feedback is a concept related to cybernetics, control, system theory, and it is in the origin of AI because it is the simplest way to understand the communication between humans and machines.

Mirchi et al. [ 11] used AI for simulation-based training in medicine, and they created a Virtual Operative Assistant to give automatic feedback to students based on metrics of performance. From a formative educational paradigm, they integrate virtual reality and AI to classify students in relation to proficiency performance benchmarks and the system gives feedback to help them improve. In the same field of medicine, we can find the results of Janpla and Piriyasurawong [ 3]. They analyze the use of AI to produce tests in e-learning environments and they develop an intelligent software to select questions for online exams.

In a different work, Saplacan et al. [ 23] suggest that feedback provided by digital systems in learning situations have some problems such as eliciting negative emotions (these are neglect, frustration, uncertainty, need for confirmation and discomfort) experienced by students in higher education. Their research used a qualitative design based on a story dialogue method and its final conclusion is that "digital interfaces should also arouse positive emotions through their design" (p. 428).

The research of Samarakou et al. [ 22] is focused on continuous monitoring and assessment (Student Diagnosis, Assistance, Evaluation System based on Artificial Intelligence, StuDiAsE) of engineering students and AI proves its usefulness to provide personalized feedback and evaluate performance with quantitative and qualitative information.

Finally, the study of Rodríguez-Ascaso et al. [ 24] is focused on adaptive learning systems and self-assessment: "In the next future, we can expect that, within adaptive e-learning systems, both automatic and manual procedures will interoperate to elicit users' interaction needs for ensuring accessibility" (p. 1). They combine a personalized system with self-assessment and the use of learning objects for people with disabilities: "students with visual, auditory, and mobility impairments, as well as non-disabled students" and "results indicate that the procedure allows students, both disabled and non-disabled, to self-assess and report adequately their preferences to access electronic learning materials" (p. 8). They found, however, some interaction problems in a number of students with visual impairments.

2.3. Other Educational Uses of AI

We can also find other works related to education [ 4], such as data management, use or management of digital resources, experiences with SEN (Special Education Needs), predicting student achievement [ 25], or simply intelligent systems to answer questions. Chatterjee and Bhattacharjee [ 26] analyze AI to manage resources on educational organizations. Liu et al. [ 27] use this type of technologies to evaluate educational quality. Other authors [ 28, 29, 30] propose AI to personalize contents for students. We can develop intelligent systems to predict academic achievement [ 31]. In the same way, Ocaña-Fernández et al. [ 13] provide data on the possibilities of promoting applications that personalize education, adjusting to the individual needs detected by artificial intelligence algorithms, and thereby providing solutions, support and educational measures that respond to adaptive education models. These authors include some examples, such as chatbots (or bots), to interact in real time with users and provide personalized information.

Considering all this theoretical framework, this study focuses on how AI is being applied at a practical level for student assessment. Although mention is made of the AI tools used, the study aims to show the results of the research at a pedagogical level, so that anyone involved in education who wants to implement AI for assessment can do so on the basis of previous research.

3. Materials and Methods

This research is based on the use of a systematic review as a method to answer a research question through a systematic and replicable process [ 32]. Specifically, the PRISMA Statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) will be used as a model for this review [ 33]. Once the inclusion selection process has been carried out, based on pre-established criteria, the main results of the selected works are codified and extracted in order to synthesize and provide an answer to the question of how artificial intelligence is being used for student assessment.

3.1. Research Questions and Objectives

Our research problem is: what is the main use of AI in education? Around this problem, we can ask these research questions: Are there studies about student assessment based on AI applications? Where do the main studies come from? What is more relevant in this research around AI, the educational approach or the technical development? What type of student assessment is based on AI?

To answer these research questions, we focus on the next research objectives:

RO1.

Identifying the main studies around student assessment based on AI in the last decade (2010-2020), using a systematic review.

RO2.

Analyzing the impact that education and/or technology have on this field of research.

RO3.

Analyzing the type of educational assessment which is being improved with AI.

3.2. Eligibility Criteria

This review includes research papers describing the use of Artificial Intelligence (AI) for student assessment, published in peer-reviewed journals, and published between 2010 and 2020. The year 2010 has been used as the starting year for the search, due to the great development of this technology derived from the beginning of the use of Siri by Apple. The languages used for the search were Spanish and English. Once the search had been carried out, the inclusion and exclusion criteria used were those set out in the following Table 1. It is important to clarify that the review is based primarily on the use of AI for student assessment in online and face-to-face subjects.

3.3. Information Sources

Studies were identified by searching electronic databases published in English and Spanish. Specifically, the search was conducted in the two main social science databases: Web of Sciences and Scopus. All relevant articles were accessed, with those that were not accessible needing to be excluded from the review.

3.4. Search Strategy

To ensure sensitivity in the search process, a co-occurrence analysis of terms was carried out to determine which terms were most common in relation to the research question [ 34]. From this analysis we concluded that the most common terms used for our search were: 'Artificial Intelligence', 'Machine Learning', 'Education', 'Assessment' and 'Evaluation'. To make the search with these terms more concrete, a protocol was designed with the combination of these terms and the use of Boolean operators. The combinations used in the two databases were:

Web of Sciences: (AB = (artificial intelligence OR machine learning) AND AB = Education AND AB = (assessment OR evaluation)). Type of document: articles. Time period: 2010-2020. Languages: English and Spanish.

Scopus: (TITLE-ABS-KEY (artificial AND intelligence) OR (TITLE-ABS-KEY (machine AND learning) AND TITLE-ABS-KEY (education) AND TITLE-ABS-KEY (assessment) OR TITLE-ABS-KEY (evaluation)). Type of document: articles. Time period: 2010-2020. Languages: English and Spanish.

The first search was conducted in March 2021 and found a total of 454 initial papers. After eliminating duplicates, a total of 449 articles were analyzed by the researchers.

3.5. Study Selection

The articles were evaluated and selected according to the above-mentioned criteria. Titles and abstracts were evaluated independently by two reviewers. Once selected, the papers were analyzed in full. When there was a discrepancy between the two reviewers, a third party assessed the proposal. To carry out the analysis of the studies, the Rayyan tool was used, which allows the review to be carried out using the "blind" mode.

3.6. Coding, Data Extraction and Analysis

For the coding of the articles, the same review tool Rayyan was used, which allows the establishment of "tags" for the categorization of the papers. For the extraction and analysis of the main information of the papers (based on the article data and the fundamental description of the use of AI for evaluation), a spreadsheet and the Mendeley manager were used.

4. Results

Of the 449 articles reviewed after eliminating duplicates, a total of 339 studies were excluded because they did not comply with the established criteria after reading the abstract and title. The full text of the remaining 100 papers was reviewed and a total of 68 were discarded for not meeting the criteria, and 10 for not having access to them. The workflow is shown in Figure 1 below.

The following Table 2 shows the main data of the selected studies: the authors, the year of publication, the journal, the authors' country of origin, their professional affiliation, and the subject or educational level at which AI was used are highlighted.

As can be seen in the table above, the journal with the most publications on this subject is the International Journal of Artificial Intelligence in Education, with up to four articles published in it. It is also striking that most of the authors are affiliated with areas of knowledge related to STEM subjects (Science, Technology, Engineering and Mathematics). However, the subjects in which AI was applied reveal, not only its use in those related to STEM, but also a wide use in language teaching, and in subjects related to health sciences. The main use of AI for assessment is focused mainly on higher education contexts, although there are some examples of its use at secondary education level [ 43, 47].

With regard to the articles in relation to their year of publication, there has been an increase in the number of publications in recent years, except in 2019 where only one article has been found. However, the year 2020 is the year in which the most papers are published in relation to this topic. Regarding the origin of the authors of the papers, it can be seen that the country which predominates in this type of study is the USA, followed by Greece. The following Table 3 lists all the authors of the selected papers.

4.1. Understanding of AI

From the analysis of the various studies, it is clear that AI is a tool that can be applied in different ways and at different levels. It is, therefore, remarkable that only two studies begin the paper by stating their perspective on what AI is. Thus, the work by Sun et al. [ 46] indicates that "artificial intelligence (AI) is the combination of intelligence, that is, machines capable of demonstrating human intelligence and of making decisions with human skills"; AI is therefore intended to "build highly advanced machines that can make smart decisions" (p. 2).

Deo et al. [ 48] determine that AI could be incorporated as a subcategory of intelligent algorithms implemented in stand-alone models, or within a hybrid integrated computer systems model. Both studies agree on the idea that they are systems that prove to be efficient in making decisions similarly to what a human would do. The AI techniques used in the studies are diverse, although the most widely used is the fuzzy-logic system [ 22, 37, 49], often in combination with other methods.

4.2. Pedagogical Model Used

From the analysis of the different studies, it can be seen that the pedagogical aspect of AI application remains relatively underrepresented. There is only one study [ 18] that clearly addresses aspects related to pedagogical models that support the work to be developed subsequently. Specifically, it is based on three key concepts for the development of the platform using AI: constraint-based modeling, Items' response theory and Evidence Centered Design. Three more articles mention pedagogical models on which the instruction is based: two use collaboration as a method of student work [ 21, 38] and the third uses argument maps [ 39].

Other works also touch on pedagogical aspects, although only descriptively in the theoretical framework. Thus, two of the works talk about this type of AI-based assessment for the development of competences in the subject [ 35, 36], three of them determine the need to give early feedback as an essential aspect in the educational process [ 22, 37, 42], one of the works determines that error detection is essential to achieve a better understanding and for pedagogical decision-making [ 40] and, finally, another work determines in the conclusions that it is necessary to incorporate a critical pedagogical stance at all levels for a better incorporation of this technology in education [ 50].

As can be seen from the studies highlighted in this section, most of those that mention pedagogical aspects are studies that are more than four years old. Recent work focuses more on the technical aspects of incorporating AI than on the pedagogical models that can underpin their use in education.

4.3. Formative Evaluation as the Reason for the Use of AI

Of the 22 selected papers, 15 have the use of AI for formative assessment as their starting point. Some of them explicitly mention this type of evaluation, while others can be extrapolated from what is described in the work. Three of the papers that do not use formative assessment focus on the use of AI in English language teaching [ 27, 44, 50], another two used AI in secondary education [ 43, 47] and the other two in mathematics, but at university level [ 48, 49].

Four studies specify that they carry out formative evaluation. In the case of Santos and Boticario [ 21], they are based on formative assessment through collaboration using a system called dotLRN. This system guides students in their collaborative work process. In the work of Goel and Joyner [ 41], it is clear that AI is a very powerful resource that allows frequent feedback to be given automatically to students and it can help teachers in this task when they have a large number of students. Maicher et al. [ 45] found that the use of AI and formative assessment can guide learners in the acquisition of competencies, in their case regarding the collection of medical information from the patient. Finally, with the work of Choi and McClenen [ 51], it is clear that for the personalization of teaching in e-learning, the incorporation of AI, in their case through the Computerized adaptive testing (CAT) method, it is a great resource to be considered.

4.4. Automated Scoring

In addition to the feedback that AI tools can provide, several studies have shown that one of the functionalities pursued is the automatic grading of students. In this way, we find up to ten studies that explicitly state that the system used grades students according to the task performed, although some of the other selected studies probably also use this tool, although it is not mentioned.

The work presented by Rhienmora et al. [ 36] is based on the assessment of competence in performing different dental techniques. The system used combines VR with AI. Based on the movements made by the user, the system evaluates his or her competence and determines a score, which leads to their categorization as either novice or expert. The study by Ouguengay et al. [ 37] assesses reading and writing skills in Amazigh by means of test assessment. The system establishes a score based on the answers given to the items. Kaila et al. [ 38], based on active learning methods, set up different types of collaboration-based activities that are automatically assessed. Goel and Joyner [ 41] in their AI-based course for teaching AI set up a system for students to automatically receive graders that allow them to quickly visualize their results and also provide feedback.

Grivokostopoulou et al. [ 42] use AI to evaluate students' performance in programing and automatically give them a grade on the task performed. Liu et al. [ 27] show how their employed AI system is able to automatically evaluate students' engineering essays in English. Jani et al. [ 20] use Machine Learning to evaluate OSCE (Observed Structured Clinical Exams) transcripts and automatically establish a score. The study by Maicher et al. [ 45] goes along the same lines by automatically assessing and grading medical students' collection of patient information. Ulum [ 50] describes the Turkish State University's application for assessing students' English language proficiency. This AI-based application establishes a grade automatically. Finally, Choi and McClenen [ 51] describe their AI system for formative assessment, which performs an automatic grading of learners to give feedback and to adapt the following tasks accordingly.

It is remarkable that most of the works that mention this automatic gradation of students are from the last years analyzed.

4.5. Comparison between IA Use and Non-Use

An essential aspect of the use of AI for assessment is its accuracy in learning compared to traditional methods. Thus, we found three studies that compare their results regarding this aspect. The work by Goel and Jyoner [ 41] makes a comparison between teaching AI from the platform they have created with this technology and another without the use of it. Their results show that students who used AI performed better than those who did not. On the other hand, the work of Grivokostopoulou et al. [ 42] made a comparison between the results obtained with AI and the teaching assessments made by hand to check the accuracy of this technology. In general, the results were very similar and correlated with each other. When analyzing the result in relation to four levels related to student grades-very low, low, medium, good, and excellent-it was observed that the results were quite similar, only finding higher results as excellent by the teacher than by the AI. The last study to mention the difference between those who used AI and those who did not is that of Samarakou et al. [ 22]. The subject in which this study was carried out was required the completion of a written assessment. From the results obtained, they determined that those who used AI scored 17% higher than those who did not use it.

5. Discussion and Conclusions

AI is today one of the main technologies being applied in all fields and at all levels. However, in education its use is not widespread, probably due to a lack of knowledge on the part of users [ 4], but it is set out to become one of the main tools to be used. As already mentioned, the main uses of AI applied to education are related to tutoring and assessment [ 40], although we also find other examples of use such as personalization [ 22, 41, 46] or quality assessment [ 20, 37, 39].

In this systematic review we have focused on analyzing the use of AI applied to student assessment based on the collection of articles published in the two main current databases: Web of Sciences and Scopus. To answer the first research objective, we can summarize that there are 454 works on AI and education in the last ten years, but after using our inclusion and exclusion criteria, we find out that only 22 studies are mainly focused on student assessment. The majority of them have been published in the last five years, and in the USA.

One of the main conclusions drawn from the analysis is that, despite the differences between human and artificial intelligence [ 7, 8], this systematic research shows us the potential to facilitate and improve education, both face to face, blended or in virtual environments.

Another of the main conclusions to be drawn from the analysis of the papers is the answer of our research objective three. We found that most of them focus on formative assessment, either implicitly or explicitly mentioning it. It is striking that three of the papers that do not use AI for formative assessment are linked to the assessment of English [ 27, 44, 50]. As for the papers that focus on formative assessment, it is clear that the idea of using AI for this task lies in the help that this technology can provide to teachers when they have a large number of students [ 41], or based on the idea that learning improves the more immediate the feedback given to students [ 21].

In some cases, this formative assessment is carried out through the automatic marking of students' work. Almost half of the works analyzed clearly describe how the AI system used automatically assesses and grades students, and how this event is used for feedback. On other occasions, the AI system used only grades the students at the end of the task, as is the case in the work of Liu et al. [ 27]. It is also clear that most of the works that mention this automatic grading are from 2019 and 2020. The fact that virtual teaching has been developed worldwide during the 2020s may have led to a breakthrough in the development of this type of technology.

Finally, it is worth noting as a conclusion of the articles analyzed how in those that make a comparison between the use of AI in one group with another that does not use it, the former obtains better results. In the same way, Grivokostopoulou et al. [ 42] show how their AI system is suitable for student evaluation and grading, as it is similar to teacher evaluation.

Despite all these advances, and what AI can do in the field of education, as Ulum's work [ 50] shows, this technology needs to be humanized. Research so far shows that a machine cannot assume the role of a teacher, and the way artificial intelligence works and carries out processes in the context of teaching is far from human intelligence [ 7] and partly due to the lack of transparency in decision-making algorithms [ 4]. In addition to this problem, we are faced with the difficulty of putting this technology into practice by all teachers. Several authors [ 8, 16, 52], have determined that AI requires specific training of our students as future professionals, because they need to understand the characteristics, possibilities and limitations of these intelligent systems. In this sense, Ocaña et al. [ 13] include diverse skills such as the following: computational thinking, programing, computing skills, and finally information and audiovisual skills. Thus, computational thinking is one of the main skills to be developed from early childhood education to prepare students for a world where programing, robotics and AI will be essential to develop their abilities [ 53]. The experiences that are being carried out in early childhood education for the development of computational thinking are essential [ 54, 55, 56]. Likewise, it is crucial to train teachers in the use of this technology [ 57], but not only on the basis of learning the tools, but also based on pedagogical reference models that give meaning to the development of classes. If we want to promote technology enhanced learning and smart environments using AI, teachers are an essential element of successful.

Author Contributions

Conceptualization, V.G.-C., P.P.-E. and R.R.-V.; Data Curation, V.G.-C., P.P.-E. and R.R.-V.; Investigation, V.G.-C., P.P.-E. and R.R.-V.; Methodology, V.G.-C.; Software, V.G.-C. and P.P.-E.; Supervision, P.P.-E.; Validation, V.G.-C., P.P.-E. and R.R.-V.; Visualization, R.R.-V.; Writing-Original Draft, V.G.-C. and P.P.-E.; Writing-Review and Editing, V.G.-C., P.P.-E. and R.R.-V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Conflicts of Interest

The authors declare no conflict of interest.

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Artificial Intelligence for Student Assessment: A Systematic Review

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Artificial Intelligence (AI) is being implemented in more and more fields, including education. The main uses of AI in education are related to tutoring and assessment. This paper analyzes the use of AI for student assessment based on a systematic review. For this purpose, a search was carried out in two databases: Scopus and Web of Science.