Artificial intelligence (AI) national strategies provide countries with a framework for the development and implementation of AI technologies. Sixty countries worldwide published their AI national strategies. The majority of these countries with more than 70% are developed countries. The approach of AI national strategies differentiates between developed and developing countries in several aspects including scientific research, education, talent development, and ethics. This paper examined AI readiness assessment in a developing country (Palestine) to help develop and identify the main pillars of the AI national strategy. AI readiness assessment was applied across education, entrepreneurship, government, and research and development sectors in Palestine (case of a developing country). In addition, it examined the legal framework and whether it is coping with trending technologies. The results revealed that Palestinians have low awareness of AI. Moreover, AI is barely used across several sectors and the legal framework is not coping with trending technologies. The results helped develop and identify the following five main pillars that Palestine’s AI national strategy should focus on: AI for Government, AI for Development, AI for Capacity Building in the private, public and technical and governmental sectors, AI and Legal Framework, and international Activities.

Choosing a university specialization is a challenging decision for high-school students. Due to the lack of guidance and limited online resources, students base their decisions on subjective perceptions of family and friends. This increases the risk of high university dropout rates, and students changing their university disciplines. To address the aforementioned drawbacks, this research paper presents a Personalized Career-path Recommender System (PCRS) to provide guidance and help high school students choose engineering discipline. The design of PCRS is based on fuzzy intelligence of N-layered architecture and uses students’ academic performance, personality type, and extra-curricular skills. The association between personality type and engineering discipline was built using a sample of 1250 engineering students enrolled in seven engineering disciplines at An-Najah National University. PCRS is implemented as a mobile application and it is tested against an evaluation sample of 177 engineers. The sample consists of graduate or undergraduate engineers who are satisfied with their engineering disciplines. The evaluation examined the agreement between the recommendations generated by PCRS and the 177 actual engineering discipline of the sample. The evaluation results proved a slight agreement between the suggested recommendations of PCRS and the actual discipline of the research sample. Hence, PCRS is capable of providing guidance to high-school students who are interested in pursuing their studies in Engineering. The PCRS application is the first career-path recommender to target Palestinian community and other developing countries in the MENA region. The design of PCRS is scalable and it can be expanded in the future to consider other academic majors of higher education.

Universities play an essential role in spreading climate change awareness. However, slight information on climate change and environmental issues had been integrated into the curricula. Moreover, minimal research had been carried out to understand university role in spreading awareness, and students level of awareness and daily behaviour towards climate change, especially in developing countries. This paper aims to investigate the aforementioned issues. An experimental study was carried out on 448 undergraduate students enrolled at An-Najah National University Palestine. The study aimed to examine students’ knowledge and daily behaviour towards climate change, and the important role the university and students’ societies play in terms of spreading and enhancing awareness. The results revealed that female and male students had a non satisfactory interest level in environmental topics and activities, and gender equality did not seem to be an issue. Moreover, female students tend to have a significantly lower level of awareness on climate change compared to male students. On the other hand, being an engineering students or a member in students’ societies had a positive impact on students’ level of awareness and especially females. The results revealed that female students who are enrolled in the engineering faculty or members of students’ societies had a significantly higher level of awareness compared to female students who are enrolled in other faculties or not members of students’ societies. In general, students had a low level of awareness regardless of gender or faculty and universities should offer undergraduate students and especially female students’ opportunities to learn more about climate change by integrating climate change topics into higher education. Moreover, universities should support extracurricular activities held by student societies, and some of these activities should be directed towards environmental and climate change issues. This study entails the activities of the Mediterranean Gender Equality Community of Practice co-created by the Mediterranean Network of Engineering Schools (RMEI), where An-Najah University is an active member, with the support of the EU TARGET project entitled ’Taking a reflexive approach to gender equality at Institutional transformation’.

Virtual Learning Environments provide teachers with a web-based platform to create different types of feedback. These environments usually follow the ’one size fits all’ approach and provide students with the same feedback. Several personalised feedback frameworks have been proposed which adapt the different types of feedback based on the student characteristics and/or the assessment question characteristics. The frameworks are intradisciplinary, neglect the characteristics of the assessment question, and either hard-code or auto-generate the types of feedback from a restricted set of solutions created by a domain expert. This paper contributes to research carried out on personalised feedback frameworks by proposing a generic novel system which is called the Ontology-based Personalised Feedback Generator (OntoPeFeGe). OntoPeFeGe addressed the aforementioned drawbacks using an ontology which is a knowledge representation of the educational domain. It integrated several generation strategies and templates to traverse the ontology and auto-generate the questions and feedback. The questions have different characteristics, in particular, they aim to assess students at different levels in Bloom’s taxonomy. Each question is associated with different types of feedback that range from verifying student’s answer towards giving the student more details related to the answer. The feedback auto-generated in OntoPeFeGe is personalised using a rule-based algorithm which takes into account the student characteristics and the assessment question characteristics. The personalised feedback in OntoPeFeGe was quantitatively evaluated on 88 undergraduate students. The results revealed that the personalised feedback significantly improved the performance of students with low background knowledge. In addition, the feedback was evaluated qualitatively using questionnaires provided to teachers and students. The results showed that teachers and students were satisfied about the feedback.

The C programming course is mandatory for undergraduate engineering students enrolled at universities in Palestine. The programming courses are taught by applying a traditional learning method in which the programming concepts are explained theoretically and minimal practical work is applied. In addition, students receive either no formative feedback or minimal manual delayed feedback after submitting their practical work. This negatively affects students’ performance as students find it difficult to understand the programming concepts. This paper addresses the aforementioned drawbacks by applying blended learning for the first time to the C programming course which is taught to more than 1000 students each year at An-Najah National University, Palestine. Blended learning is a hybrid learning method which integrates the traditional learning method with technology and online learning. It provides students with more practical work and automated immediate formative feedback. This paper aims to study the effect of blended learning on students’ performance, and students’ satisfaction with the blended learning method. An independent experimental design was applied on 1374 undergraduate students enrolled in C programming course. Students were divided into two groups based on the learning method. The first group consisted of 632 students who studied the course using the traditional learning method, while the second group consisted of 742 students in which blended learning was applied. Quantitative and qualitative research methods were applied and the results revealed that blended learning improved student performance significantly compared with traditional learning. In addition, the results revealed that students were satisfied with the blended learning method in terms of easiness to use and suitability for programming and submitting assignments.

An ontology is a knowledge representation structure which has been used in Virtual Learning Environments (VLEs) to describe educational courses by capturing the concepts and the relationships between them. Several ontology-based question generators used ontologies to auto-generate questions, which aimed to assess students’ at different levels in Bloom’s taxonomy. However, the evaluation of the questions was confined to measuring the qualitative satisfaction of domain experts and students. None of the question generators tested the questions on students and analysed the quality of the auto-generated questions by examining the question’s difficulty, and the question’s ability to discriminate between high ability and low ability students. The lack of quantitative analysis resulted in having no evidence on the quality of questions, and how the quality is affected by the ontology-based generation strategies, and the level of question in Bloom’s taxonomy (determined by the question’s stem templates). This paper presents an experiment carried out to address the drawbacks mentioned above by achieving two objectives. First, it assesses the auto-generated questions’ difficulty, discrimination, and reliability using two statistical methods: Classical Test Theory (CTT) and Item Response Theory (IRT). Second, it studies the effect of the ontology-based generation strategies and the level of the questions in Bloom’s taxonomy on the quality of the questions. This will provide guidance for developers and researchers working in the field of ontology-based question generators, and help building a prediction model using machine learning techniques.