Adcock, AB, & Eck, RNV (2005). Reliability and factor structure of the attitude toward tutoring agent scale (attas). Journal of Interactive Learning Research, 16(2), 195.
Aleven, V, & Koedinger, K (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26(2), 147–179.
Aleven, V, Ogan, A, Popescu, O (2004). Evaluating the effectiveness of a tutorial dialogue system for self-explanation. In Intelligent Tutoring Systems (ITS) 2004. Springer, New York, (pp. 443–454).
Aleven, V, McLaren, BM, Sewall, J, Koedinger, KR (2009). A new paradigm for intelligent tutoring systems: example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105–154.
Atkinson, RK, Renkl, A, Merrill, MM (2003). Transitioning from studying examples to solving problems: effects of self-explanation prompts and fading worked-out steps. Journal of Educational Psychology, 95(4), 774.
Beck, JE, & Gong, Y (2013). Wheel-spinning: students who fail to master a skill. In International Conference on Artificial Intelligence in Education. Springer, Heidelberg, (pp. 431–440).
Blackwell, LS, Trzesniewski, KH, Dweck, CS (2007). Implicit theories of intelligence predict achievement across an adolescent transition: a longitudinal study and an intervention. Child Development, 78(1), 246–263.
Bloom, BS. (1956). Taxonomy of educational objectives: the classification of educational goals. Harlow: Longman Group.
Chi, M, & VanLehn, K (2010). Meta-cognitive strategy instruction in intelligent tutoring systems: how, when, and why. Educational Technology and Society, 13(1), 25–39.
Chi, MT (2009). Active-constructive-interactive: a conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1(1), 73–105.
Craig, SD, Driscoll, DM, Gholson, B (2004). Constructing knowledge from dialog in an intelligent tutoring system: interactive learning, vicarious learning, and pedagogical agents. Journal of Educational Multimedia and Hypermedia, 13(2), 163.
Craig, SD, Hu, X, Graesser, AC, Bargagliotti, AE, Sterbinsky, A, Cheney, KR, Okwumabua, T (2013). The impact of a technology-based mathematics after-school program using aleks on student’s knowledge and behaviors. Computers & Education, 68, 495–504.
Epstein, J (2014). Basic skills diagnostic test. www.flaguide.org/tools/diagnostic/basic_skills_diagnostic.php. Accessed 15 June 2014.
Falmagne, JC, Albert, D, Doble, C, Eppstein, D, Hu, X, (Eds.) (2013). Knowledge spaces. Berlin, Germany: Springer.
Graesser, AC, Langston, MC, Lang, KL (1992). Designing educational software around questioning. Journal of Interactive Learning Research, 3(2), 235.
Graesser, AC, D’Mello, SK, Hu, X, Cai, Z, Olney, A, Morgan, B (2012). AutoTutor. In: McCarthy, PM, & Boonthum, C (Eds.) In Applied natural language processing and content analysis: identification, investigation and resolution. IGI Global, Hershey, PA, (pp. 169–187).
Heidig, S, & Clarebout, G (2011). Do pedagogical agents make a difference to student motivation and learning. Educational Research Review, 6(1), 27–54.
Huang, X, Craig, SD, Xie, J, Graesser, A, Hu, X (2016). Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences, 47, 258–265.
Kulik, JA, & Fletcher, J (2016). Effectiveness of intelligent tutoring systems: a meta-analytic review. Review of Educational Research, 86(1), 42–78.
Landauer, TK, Foltz, PW, Laham, D (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259–284.
Lehman, B, D’Mello, S, Graesser, A (2012). Interventions to regulate confusion during learning. In Proceedings of the 11th international conference on Intelligent Tutoring Systems. Springer-Verlag, Berlin, Heidelberg, ITS’12, (pp. 576–578).
Nye, BD (2016). Its, the end of the world as we know it: transitioning aied into a service-oriented ecosystem. International Journal of Artificial Intelligence in Education, 26(2), 756–770.
Nye, BD, Graesser, AC, Hu, X (2013). Multimedia learning in intelligent tutoring systems. In: Mayer, RE (Ed.) In Multimedia Learning. 3rd Ed. Cambridge University Press, New York.
Nye, BD, Graesser, AC, Hu, X (2014a). AutoTutor and family: a review of 17 years of science and math tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469.
Nye, BD, Graesser, AC, Hu, X, Cai, Z (2014b). AutoTutor in the cloud: a service-oriented paradigm for an interoperable natural-language its. Journal of Advanced Distributed Learning Technology, 2(6), 35–48.
Pane, JF, Griffin, BA, McCaffrey, DF, Karam, R (2014). Effectiveness of cognitive tutor algebra i at scale. Educational Evaluation and Policy Analysis, 36(2), 127–144.
Pintrich, PR, Smith, DA, García, T, McKeachie, WJ (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (mslq). Educational and Psychological Measurement, 53(3), 801–813.
Renkl, A (2005). The worked-out-example principle in multimedia learning. In: Mayer, RE (Ed.) In The Cambridge handbook of multimedia learning. Cambridge University Press, New York, (pp. 229–245).
Roschelle, J, & Kaput, J (1996). Educational software architecture and systemic impact: the promise of component software. Journal of Educational Computing Research, 14(3), 217–228.
Sabo, KE, Atkinson, RK, Barrus, AL, Joseph, SS, Perez, RS (2013). Searching for the two sigma advantage: evaluating algebra intelligent tutors. Computers in Human Behavior, 29(4), 1833–1840.
Schommer-Aikins, M, Duell, OK, Hutter, R (2005). Epistemological beliefs, mathematical problem-solving beliefs, and academic performance of middle school students. The Elementary School Journal, 105(3), 289–304.
Schroeder, NL, Adesope, OO, Gilbert, RB (2013). How effective are pedagogical agents for learning? A meta-analytic review. Journal of Educational Computing Research, 49(1), 1–39.
Schwonke, R, Renkl, A, Krieg, C, Wittwer, J, Aleven, V, Salden, R (2009). The worked-example effect: not an artefact of lousy control conditions. Computers in Human Behavior, 25(2, SI), 258–266.
Schworm, S, & Renkl, A (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology, 99(2), 285.
Sullins, J, Meister, R, Craig, S, Wilson, W, Bargagliotti, A, Hu, X (2013). Is there a relationship between interacting with a mathematical intelligent tutoring system and students performance on standardized high-stake tests. Knowledge Spaces: Applications to education, 69–78.
VanLehn, K (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227–265.
VanLehn, K (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
VanLehn, K, Siler, S, Murray, C, Yamauchi, T, Baggett, WB (2003). Why do only some events cause learning during human tutoring?Cognition and Instruction, 21(3), 209–249.
Vanlehn, K, Graesser, AC, Jackson, GT, Jordan, P, Olney, A, Rosé, CP (2007). When are tutorial dialogues more effective than reading?Cognitive Science, 31(1), 3–62. http://www.ncbi.nlm.nih.gov/pubmed/21635287.
Venkatesh, V, Morris, MG, Davis, GB, Davis, FD (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478.
Willingham, DT. (2009). Why don’t students like school?: a cognitive scientist answers questions about how the mind works and what it means for the classroom. San Francisco: Wiley.