Thanks to advances in AI and machine learning, a slow but steady transformation is coming to education -- under the hood.

This article focuses on contributions that AI can make to address longterm educational goals. Challenges are described that support: (1) mentors for every learner; (2) learning 21st century skills; (3) interaction data for learning; (4) universal access to global classrooms; and (5) lifelong and lifewide learning. A vision and brief research agenda are described for each challenge along with goals that lead to development of global educational resources and the reuse and sharing of digital educational resources. Instructional systems with AI technology are described that currently support richer experiences for learners and supply researchers with new opportunities to analyze vast data sets of instructional behavior from big databases that record elements of learning, affect, motivation, and social interaction. Personalized learning is described that facilitates student and group experience, reflection, and assessment.

This article focuses on contributions that AI can make to address long-term educational goals. It describes five challenges that would support: (1) mentors for every learner; (2) learning twenty-first century skills; (3) interaction data to support learning; (4) universal access to global classrooms; and (5) lifelong and life-wide learning. A vision and brief research agenda are described for each challenge along with goals that lead to access to global educational resources and the reuse and sharing of digital educational resources. Instructional systems with AI technology are described that currently support richer experiences for learners and supply researchers with new opportunities to analyze vast data sets of instructional behavior from big databases, containing elements of learning, affect, motivation, and social interaction. Personalized learning is described using computational tools that enhance student and group experience, reflection, and analysis, and supply data for development of novel theory development.

Learner assessment is nontrivial even in its most basic incarnation, namely evaluating a learner's understanding of a set of domain-dependent skills from ad hoc test items (for example, Desmarais [2011]). The assessment challenges increase with the complexity of the learner's traits to be captured, because how a student behaves during an instructional activity generally provides partial and ambiguous information on the student's underlying states, and the gap between what can be observed and what a learner actually thinks and feels increases as these states go from cognitive to metacognitive and affective. In ITSs, the research field concerned with addressing these challenges is known as student modeling, and a student model is the ITS component in charge of assessing student traits and states relevant to tailor the tutorial interaction to specific student needs. Student modeling research has made the problem solution from the tutor et al. [2010]), given extensive evidence substantial progress in providing reliable (for instance by repeatedly asking for in education research showing that learner assessment during problem help) without trying to solve the problem affective factors play an important role solving or question-answering on their own (Baker et al. 2008), in learning. Educational technology At the cognitive level, knowledge can foster understanding at different however, continues to produce novel assessment, that is, evaluating the student's stages of the learning process or for environments often consisting of knowledge of relevant concepts learners with different preferences and activities not as structured and well and skills at specific points of the interaction abilities.

Artificial intelligence has pioneered new technologies in the education for classroom engagements and in school systems on a broader dimension with huge potential to promote education. Haugeland defines AI as the exciting new effort to make computers think… machines with minds, in the full and literal sense. This article focuses on engineering education in a knowledge society with effectiveness in view. It examines the technologies in current use, applications, and future possibilities. It concludes that effectiveness is a continuously improvable process as we iterate towards a desirable future. Today's education model largely focuses on one instructor providing information to several learners at the same time.