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This work describes a learning tool named ARGeoITS that combines augmented reality with an intelligent tutoring system to support geometry learning. The work depicts a study developed in Mexico to measure the impact on the learning and motivation of students using two different learning systems: an intelligent tutoring system with augmented reality (ARGeoITS) and a system with only augmented reality (ARGeo). To study the effect of this type of technology (ARGeoITS, ARGeo) and time of assessment (pre-, post-) on learning gains and motivation, we applied a 2 × 2 factorial design to 106 middle school students. Both pretest and post-test questionnaires were applied to each group to determine the students’ learning gains, as was an IMMS motivational survey to evaluate the students’ motivation. The results show that: (1) students who used the intelligent tutoring system ARGeoITS scored higher in learning gain (7.47) compared with those who used ARGeo (6.83); and (2) both the ARGeoITS and ARGeo learning tools have a positive impact on students’ motivation. The research findings imply that intelligent tutoring systems that integrate augmented reality can be exploited as an effective learning environment to help middle–high school students learn difficult topics such as geometry.

Augmented reality (AR)-based learning environments are argued to foster cognitive and emotional involvement. Immersion has been identified as one of the driving forces that promote learning in technology-based learning environments. This study evaluated the learning effectiveness and immersion appeal of an AR-marker-based learning activity targeted at practicing basic chemistry concepts. Data were collected from a cohort of 124 middle school students in Mexico and analyzed using pretest–posttest comparisons and cluster analysis. The results suggest that students with higher immersive learning profiles achieve better learning outcomes compared with those with lower immersive profiles. The authors acknowledge the eMadrid Network, which is funded by the Madrid Regional Government (Comunidad de Madrid) with grant No. P2018/TCS-4307. This work also received partial support from FEDER/Ministerio de Ciencia, Innovación y Universidades—Agencia Estatal de Investigación through project Smartlet (TIN2017-85179-C3-1-R) and received partial support from MCIN/AEI/10.13039/501100011033. The publication is part of the I+D+i project “H2O Learn” (PID2020-112584RB-C31). We also acknowledge the program PRODEP in Mexico that supported this work (511-6/2019-8474).