In this paper we propose myPTutor, a general and effective approach which uses Al planning techniques to create fully tailored learning routes, as sequences of Learning Objects (LOs) that fit the pedagogical and students' requirements.\ud \ud myPTutor has a potential applicability to support e-learning personalization by producing, and automatically solving, a planning model from (and to) e-learning standards in a vast number of real scenarios, from small to medium large e-learning communities. Our experiments demonstrate that we can solve scenarios with large courses and a high number of students. Therefore, it is perfectly valid for schools, high schools and universities, especially if they already use Moodle, on top of which we have implemented myPTutor. It is also of practical significance for repairing unexpected discrepancies (while the students are executing their learning routes) by using a Case-Based Planning adaptation process that reduces the differences between the original and the new route, thus enhancing the learning process

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Cambridge University Press (CUP)Garrido Tejero, A.; Morales, L.; Fernandez, S.; Onaindia De La Rivaherrera, E.; Morales, L.; Borrajo, D.; Castillo, L. (2013). On the automatic compilation of e-learning models to planning. Knowledge Engineering Review. 28(2):121-136. doi:10.1017/S0269888912000380.On the automatic compilation of e-learning models to planning AbstractThis paper presents a general approach to automatically compile e-learning models to planning, allowing us to easily generate plans, in the form of learning designs, by using existing domainindependent planners. The idea is to compile, first, a course defined in a standard e-learning language into a planning domain, and, second, a file containing students learning information into a planning problem. We provide a common compilation and extend it to three particular approaches that cover a full spectrum of planning paradigms, which increases the possibilities of using current planners: i) hierarchical, ii) including PDDL actions with conditional effects, and iii) including PDDL durative actions. The learning designs are automatically generated from the plans and can be uploaded, and subsequently executed, by learning management platforms. We also provide an extensive analysis of the e-learning metadata specification required for planning, and the pros and cons on the knowledge engineering procedures used in each of the three compilations. Finally, we include some qualitative and quantitative experimentation of the compilations in several domain-independent planners to measure its scalability and applicability.

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