R. John, . Anderson, T. Albert, . Corbett, R. Kenneth et al., Cognitive tutors: Lessons learned. The journal of the learning sciences, pp.167-207, 1995.

P. Auer, N. Cesa-bianchi, Y. Freund, and R. E. Schapire, The Nonstochastic Multiarmed Bandit Problem, SIAM Journal on Computing, vol.32, issue.1, pp.48-77, 2003.
DOI : 10.1137/S0097539701398375

S. Ryan, . Baker, T. Albert, V. Corbett, and . Aleven, More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing, Intelligent Tutoring Systems, pp.406-415, 2008.

J. Frank, T. Balbach, and . Zeugmann, Recent developments in algorithmic teaching, Inter. Conf. on Language and Automata Theory and Applications, 2009.

E. Joseph, K. Beck, and . Chang, Identifiability: A fundamental problem of student modeling, User Modeling 2007, pp.137-146, 2007.

E. Joseph, X. Beck, and . Xiong, Limits to accuracy: How well can we do at student modeling? In Educational Data Mining, 2013.

D. E. Berlyne, Conflict, arousal, and curiosity, 1960.
DOI : 10.1037/11164-000

E. Brunskill and S. Russell, Rapid: A reachable anytime planner for imprecisely-sensed domains. arXiv preprint, 2012.

S. Bubeck-andnicoì-o-cesa-bianchi, Regret analysis of stochastic and nonstochastic multi-armed bandit problems, Foundations and Trends R in Stochastic Systems, vol.1, issue.4, p.2012

M. Cakmak and M. Lopes, Algorithmic and human teaching of sequential decision tasks, AAAI Conference on Artificial Intelligence (AAAI'12), 2012.
URL : https://hal.archives-ouvertes.fr/hal-00755253

H. Cen, K. R. Koedinger, and B. Junker, Is over practice necessary?improving learning efficiency with the cognitive tutor through educational data mining, Frontiers in Artificial Intelligence and Applications, vol.158, p.511, 2007.

K. Hao-cen, B. Koedinger, and . Junker, Learning factors analysis?a general method for cognitive model evaluation and improvement Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, Intelligent Tutoring Systems, pp.164-175253, 1994.

I. Selega and C. , Optimal experience: Psychological studies of flow in consciousness, 1992.

J. Davenport, A. Rafferty, M. Timms, D. Yaron, and M. Karabinos, Chemvlab+: evaluating a virtual lab tutor for high school chemistry, Inter. Conf. of the Learning Sciences (ICLS), 2012.

S. Engeser and F. Rheinberg, Flow, performance and moderators of challenge-skill balance, Motivation and Emotion, vol.99, issue.3, pp.158-172, 2008.
DOI : 10.1007/s11031-008-9102-4

S. A. Goldman and M. J. Kearns, On the Complexity of Teaching, Journal of Computer and System Sciences, vol.50, issue.1, pp.20-31, 1995.
DOI : 10.1006/jcss.1995.1003

P. José, J. González-brenes, and . Mostow, Dynamic cognitive tracing: Towards unified discovery of student and cognitive models, In EDM, pp.49-56, 2012.

J. Gottlieb, P. Oudeyer, M. Lopes, and A. Baranes, Information-seeking, curiosity, and attention: computational and neural mechanisms, Trends in Cognitive Sciences, vol.17, issue.11, pp.585-593, 2013.
DOI : 10.1016/j.tics.2013.09.001

URL : https://hal.archives-ouvertes.fr/hal-00913646

R. Kenneth, E. Koedinger, R. S. Brunskill, . Baker, A. Elizabeth et al., New potentials for data-driven intelligent tutoring system development and optimization, 2013.

K. R. Koedinger, J. R. Anderson, W. H. Hadley, and M. A. Mark, Intelligent tutoring goes to school in the big city. Inter, Journal of Artificial Intelligence in Education (IJAIED), vol.8, pp.30-43, 1997.
URL : https://hal.archives-ouvertes.fr/hal-00197383

D. Carol and . Lee, Signifying in the zone of proximal development. An introduction to Vygotsky, pp.253-284, 2005.

J. I. Lee and E. Brunskill, The impact on individualizing student models on necessary practice opportunities, Inter. Conf. on Educational Data Mining (EDM), 2012.

M. Lopes, B. Clement, D. Roy, and P. Oudeyer, Multi-armed bandits for intelligent tutoring systems. arxiv, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00913669

M. Lopes and P. Oudeyer, The strategic student approach for life-long exploration and learning, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2012.
DOI : 10.1109/DevLrn.2012.6400807

URL : https://hal.archives-ouvertes.fr/hal-00755216

R. Nkambou, R. Mizoguchi, and J. Bourdeau, Advances in intelligent tutoring systems, 2010.
DOI : 10.1007/978-3-642-14363-2

URL : https://hal.archives-ouvertes.fr/hal-00699845

A. Rafferty, E. Brunskill, T. Griffiths, and P. Shafto, Faster Teaching by POMDP Planning, In Artificial Intelligence in Education, vol.32, issue.1, pp.280-287, 2011.
DOI : 10.1037/h0044672

A. N. Rafferty, M. M. Lamar, and T. L. Griffiths, Inferring learners knowledge from observed actions, Inter. Conf. on Educational Data Mining (EDM), 2012.
DOI : 10.1111/cogs.12157

D. Roy, Usage d'un robot pour la remédiation en mathématiques, 2012.

M. Richard, . Ryan, L. Edward, and . Deci, Intrinsic and extrinsic motivations: Classic definitions and new directions, Contemporary educational psychology, vol.25, issue.1, pp.54-67, 2000.

Y. Semet, Y. Yamont, . Biojout, P. Luton, and . Collet, Artificial ant colonies and e-learning: An optimisation of pedagogical paths, International Conference on Human-Computer Interaction, 2003.

J. Valerie and . Shute, Stealth assessment in computer-based games to support learning. Computer games and instruction, pp.503-524, 2011.