Checking date: 20/05/2023

Course: 2023/2024

AI in Education
Master in Applied Artificial Intelligence (Plan: 475 - Estudio: 378)

Coordinating teacher: MUÑOZ MERINO, PEDRO JOSE

Department assigned to the subject: Telematic Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
- Basic knowledge about statistics and probability - Basic knowledge about programming
- Know the main applications that use data and artificial intelligence in education. - Know conversational tutors that assits during the educational process - Know how to use methods to infer intelligent information about students based on their interactions in learning platforms. - Know how adaptive learning applications work. - Know how predictive systems in education work. - Know how to evaluate educational systems.
Skills and learning outcomes
Description of contents: programme
1.- Introduction to learning analytics and applications in education of the use of data 2.- User models 2.1.- Skill models, meta-cognitive models, and affective models 2.2.- Models based on knowledge engineering 2.3.- Models based on probabilistic methods 2.4.- Models based on ontologies 2.5.- Models based on text mining 3.- Adaptative learning 3.1.- Components of an adaptive system 3.2.- Adaptation methods 4.- Predictive systems in education 4.1.- Purposes 4.2.- Methods: regression, random forest, neural networks, etc. 4.3.- Validation and evaluation of the models 5.- Conversational intelligent tutors 6. Evaluation of learning systems 5.1.- Pattern discovery with clustering techniques 5.2.- Comparison between systems or system vs human tutor 5.3.- Evaluation of usability 5.4.- Evaluation of effectiveness and impact 5.5.- Evaluation of other indicators
Learning activities and methodology
The training activities consist of: - AF1: Theoretical sessions - AF2: Practical sessions - AF5: Office hours - AF6: Work in group - AF7: Individual work of the student - AF8: Final exam The methodologies will include: - MD1: Presentations in teaching sessions by the professor with the support of computer and audiovisual media, in which the main concepts of the subject and the bibliography is provided to complement the learning of the students. - MD2: Critical reading of texts recommended by the professor: scientific articles. - MD3 Resolution of practical cases, problems, etc... proposed by the teacher in a group - MD4: Presentation and discussion in class, under the moderation of the teacher, of issues related to the content of the subject, as well as practical cases - MD5: Preparation of work and reports individually or in groups
Assessment System
  • % end-of-term-examination 30
  • % of continuous assessment (assigments, laboratory, practicals...) 70
Calendar of Continuous assessment
Basic Bibliography
  • Lang, C., Siemens, G., Wise, A., & Gasevic, D. (Eds.). Handbook of learning analytics. . New York: SOLAR, Society for Learning Analytics and Research.. 2017
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.