The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms
Open accessAbstract
Recently, the HCI community has seen increased interest in the design of teaching augmentation (TA): tools that extend and complement teachers' pedagogical abilities during ongoing classroom activities. Examples of TA systems are emerging across multiple disciplines, taking various forms: e.g., ambient displays, wearables, or learning analytics dashboards. However, these diverse examples have not been analyzed together to derive more fundamental insights into the design of teaching augmentation. Addressing this opportunity, we broadly synthesize existing cases to propose the TA framework. Our framework specifies a rich design space in five dimensions, to support the design and analysis of teaching augmentation. We contextualize the framework using existing designs cases, to surface underlying design trade-offs: for example, balancing actionability of presented information with teachers' needs for professional autonomy, or balancing unobtrusiveness with informativeness in the design of TA systems. Applying the TA framework, we identify opportunities for future research and design.
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By calling teachers' attention to discrepancies between their own judgments and those of an AIEd system, the system may promote productive teacher reflection and learning.
“By calling teachers' attention to discrepancies between their own judgments and those of an AIEd system, the system may promote productive teacher reflection and learning”
From Equity and Artificial Intelligence in Education: Will "AIEd" Amplify or Alleviate Inequities in Education? (2021) · cites this paper as An, Holstein, et al., 2020
The Methods section discusses the tension between teachers’ contextual judgments and a system’s higher-level interpretations, and the Discussion describes a design that prompts teachers to reflect in action. The passages do not say that calling attention to discrepancies between teacher and AIEd judgments promotes teacher reflection and learning; that specific mechanism is not supported. (LLM, conf=0.75)
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Towards Collaboration Translucence
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“y not be shared with the whole class [51, 52]. At the same time, teachers and students have expressed needs for some level of shared visibility during class, in order to promote mutual accountability [11, 33], to help motivate students through competition or cooperation [4, 52] or to facilitate students to support pee…”
BackgroundInfluentialLeony, D., Pardo, A., de‐la‐Fuente‐Valentín, L., de Castro, D. S., & Kloos, C. D. (2012) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2330601.2330642 · Cited by 90 · see citing works
Walker, E., Rummel, N., & Koedinger, K. R. (2011) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-011-9111-2 · Cited by 90 · cited 1× · see citing works
“…the same time, teachers and students have expressed needs for some level of shared visibility during class, in order to promote mutual accountability [11, 33], to help motivate students through competition or cooperation [4, 52] or to facilitate students to support peers in need of help [52, 132].”
BackgroundMonitoring, awareness and reflection in blended technology enhanced learning: a systematic review
Rodríguez‐Triana, M. J., Prieto, L. P., Vozniuk, A., Boroujeni, M. S., Schwendimann, B. A., Holzer, A., & Gillet, D. (2017) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2017.084489 · Cited by 89 · cited 4× · see citing works
“Furthermore, TA tools have been designed to support a range of teacher processes, many of which are covered by the notion of classroom orchestration [30, 71, 125], such as monitoring [61, 107, 118, 122] and decision-making [12, 90, 92, 135].”
BackgroundInfluentialKnight, S., & Shum, S. B. (2017) · Society for Learning Analytics Research (SoLAR) eBooks · doi:10.18608/hla17.001 · Cited by 88 · cited 1× · see citing works
“No TA system can be truly agnostic [33, 45, 67]: no matter how simple and direct an information display may be, its design will always represent a particular framing of ongoing classroom events (e.g., by the mere act of including certain kinds of information while excluding others).”
BackgroundAn Ambient Awareness Tool for Supporting Supervised Collaborative Problem Solving
Alavi, H. S., & Dillenbourg, P. (2012) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2012.7 · Cited by 88 · cited 5× · see citing works
“ity to monitor critical yet implicit aspect of their own ongoing behavior: their distribution of proximity across students while circulating around the [10]. Similarly, systems classroom like Lantern [4] and MTDashboard [82] extend a teacher’s ability to monitor students’ progress or performance during ongoing classro…”
BackgroundInfluentialXhakaj, F., Aleven, V., & McLaren, B. M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66610-5_23 · Cited by 81 · see citing works
Jin, Y., Fawcett, A. M., & Fawcett, S. E. (2013) · International Journal of Physical Distribution & Logistics Management · doi:10.1108/ijpdlm-10-2011-0169 · Cited by 80 · see citing works
Augmenting Human Intellect and Amplifying Perception and Cognition
Schmidt, A. (2017) · IEEE Pervasive Computing · doi:10.1109/mprv.2017.8 · Cited by 70 · cited 1× · see citing works
“doi.org/10.1145/3313831.3376277 In this paper, we frame these diverse technologies broadly as real-time teaching augmentation (TA). In the spirit of prior HCI work on intelligence augmentation (e.g., [35, 73, 114]), we define TA systems as technologies that extend and complement teachers’ pedagogical capabilities in a…”
BackgroundInfluentialEcheverria, V., Martinez-Maldonado, R., Buckingham Shum, S., Chiluiza, K., Granda, R., & Conati, C. (2018) · Journal of Learning Analytics · doi:10.18608/jla.2018.53.6 · Cited by 70 · cited 1× · see citing works
“Yet at present, the attentional continuum [14, 16] is rarely an explicit consideration in the design of teacher-facing classroom technologies [9, 10, 34].”
BackgroundMoraveji, N., Morris, M. R., Morris, D., Czerwinski, M. P., & Riche, N. H. (2011) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979203 · Cited by 69 · see citing works
Learning and Becoming in Practice: The International Conference of the Learning Sciences (ICLS) 2014
Polman, J. L., Kyza, E. Α., Kevin, D., et al. (2014) · Cited by 68 · see citing works
Informing Teachers Live about Student Learning: Reporting in Assistment System
Feng, M., & Heffernan, N. T. (2005) · Cited by 66 · see citing works
An augmented lecture feedback system to support learner and teacher communication
Zarraonandía, T., Aedo, I., Dı́az, P., et al. (2013) · British Journal of Educational Technology · doi:10.1111/bjet.12047 · Cited by 61 · cited 3× · see citing works
“For example, Lumilo’s smart glasses interface (or other wearable systems [19, 58, 103, 143]) ensures that the information provided to teachers remains private from students or others present in the classroom.”
BackgroundRodríguez‐Triana, M. J., Prieto, L. P., Martínez‐Monés, A., Asensio-Pérez, J. I., & Dimitriadis, Y. A. (2018) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3170358.3170364 · Cited by 61 · see citing works
Bakker, S., & Niemantsverdriet, K. (2016) · TU/e Research Portal · Cited by 55 · see citing works
Self-citationHuman-in-the-Loop Interpretability PriorPreprint
Lage, I., Ross, A. S., Kim, B., Gershman, S. J., & Doshi‐Velez, F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11571 · Cited by 55 · cited 2× · see citing works
“Finally, designers should consider ways to allow teachers to meaningfully participate in shaping a TA system’s interpretations [68, 106].”
BackgroundCreating Effective Learning Analytics Dashboards: Lessons Learnt
Charleer, S., Klerkx, J., Duval, E., Laet, T. D., & Verbert, K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45153-4_4 · Cited by 54 · cited 3× · see citing works
“[25] Charleer, S.”
BackgroundApplying Learner-Centered Principles in Teacher Education
Pierce, J. W., & Kalkman, D. L. (2003) · Theory Into Practice · doi:10.1207/s15430421tip4202_6 · Cited by 52 · cited 1× · see citing works
“With an increased emphasis on personalized learning [96], teachers are now tasked with even greater challenges in tracking students’ learning processes and adapting accordingly [37, 97].”
BackgroundMartínez‐Maldonado, R., Pardo, A., Mirriahi, N., Yacef, K., Kay, J., & Clayphan, A. J. (2015) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2723576.2723583 · Cited by 50 · see citing works
Decision Tree Modeling of Wheel- Spinning and Productive Persistence in Skill Builders
Kai, S., Almeda, M. V., Baker, R. S., Heffernan, C., & Heffernan, N. T. (2018) · Journal of Educational Data Mining · doi:10.5281/zenodo.3344810 · Cited by 47 · cited 1× · see citing works
“how frequently a student is making errors on a learning task, teachers might want support in interpreting whether the student truly needs their help (e.g., whether or not this student is struggling ) [51, 54, 62]. productively Given these trade-offs, a core design challenge for TA systems is to find the right “blend”…”
BackgroundDesign and evaluation of teacher assistance tools for exploratory learning environments
Mavrikis, M., Gutiérrez-Santos, S., & Poulovassilis, A. (2016) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2883851.2883909 · Cited by 44 · cited 4× · see citing works
“TA systems have been developed across a range of disciplines, including HCI [11, 12, 66], learning analytics [83, 86, 127], teacher education [58, 111, 112], AI in education [40, 55, 125, 141], and the learning sciences [71, 122].”
BackgroundMethodInfluentialChallenges in Using Big Data to Develop Decision Support Systems for Social Work in Germany
Schneider, D., & Seelmeyer, U. (2019) · Journal of Technology in Human Services · doi:10.1080/15228835.2019.1614513 · Cited by 44 · see citing works
An, P., Bakker, S., Ordanovski, S., Taconis, R., Paffen, C. L. E., & Eggen, B. H. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300321 · Cited by 44 · cited 20× · see citing works
“LEFT: ambient information on light objects supporting teachers’ (a) division of time and attention over students [10] and (b) monitoring of computer-supported learning processes [12].”
BackgroundMethodInfluentialSelf-citationAsk Not What AI Can Do, But What AI Should Do: Towards a Framework of Task DelegabilityPreprint
Lubars, B., & Tan, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.03245 · Cited by 43 · see citing works
Bakker, S., van den Hoven, E., & Eggen, B. H. (2013) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/2460625.2460634 · Cited by 42 · cited 7× · see citing works
“Similarly, the FireFlies systems [11–13, 129] (two examples of which are reviewed in the next section) support teachers by opening up nonverbal communication channels between the teacher and students via the color of each student/team’s light object.”
BackgroundMethodInfluentialSelf-citationExploring peripheral interaction design for primary school teachers
Bakker, S., van den Hoven, E., Eggen, B. H., et al. (2012) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/2148131.2148184 · Cited by 42 · cited 4× · see citing works
“To date, relatively few projects have explored the design of peripheral TA features (e.g., [4, 15]).”
BackgroundInfluentialSelf-citationUnselfconscious Interaction: A Conceptual Construct
Wakkary, R., Desjardins, A., & Hauser, S. (2015) · Interacting with Computers · doi:10.1093/iwc/iwv018 · Cited by 41 · cited 2× · see citing works
“[130] Wakkary, R.”
BackgroundMartínez‐Maldonado, R., Kay, J., Yacef, K., et al. (2013) · Computer Supported Collaborative Learning · doi:10.22318/cscl2013.1.320 · Cited by 40 · see citing works
Intelligent teaching assistant systems
Yacef, K. (2003) · doi:10.1109/cie.2002.1185885 · Cited by 39 · cited 3× · see citing works
“[141] Yacef, K.”
BackgroundStudying Teacher Orchestration Load in Technology-Enhanced Classrooms
Prieto, L. P., Sharma, K., & Dillenbourg, P. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24258-3_20 · Cited by 39 · cited 1× · see citing works
“This dimension is critical to consider given how scarce a teacher’s attention can be during an ongoing class session [9, 50], as also demonstrated in research on orchestration load [101, 102].”
BackgroundAn, P., Bakker, S., Ordanovski, S., Taconis, R., & Eggen, B. H. (2018) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/3173225.3173243 · Cited by 37 · cited 10× · see citing works
“Similarly, the FireFlies systems [11–13, 129] (two examples of which are reviewed in the next section) support teachers by opening up nonverbal communication channels between the teacher and students via the color of each student/team’s light object.”
BackgroundInfluentialSelf-citationDesigning for Awareness in Interactions with Shared Systems
Niemantsverdriet, K., van Essen, H., Pakanen, M., & Eggen, B. H. (2019) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3338845 · Cited by 36 · cited 2× · see citing works
“…design of TA systems is to anticipate student and teacher boundaries regarding the social visibility of real-time information in the classroom, and to design forms of shared awareness [91] that can effectively support student motivation and collaboration without crossing these boundaries [52, 93].”
BackgroundSelf-citationEconomy of China Analysis and Forecast (2019)
Press（China）, S. S. A., Yang, L., & Ping, L. (2019) · Social Sciences Academic Press（China） eBooks · doi:10.31193/ssap.01.9787509752807 · Cited by 32 · see citing works
Ahn, J., Clegg, T., Yip, J. C., Bonsignore, E., Pauw, D., Cabrera, L., Hernly, K., Pitt, C. R., Mills, K., Salazar, A., et al. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173852 · Cited by 32 · see citing works
"I Spent More Time with that Team"
Martínez‐Maldonado, R. (2019) · International Conference on Learning Analytics & Knowledge (LAK) · doi:10.1145/3303772.3303818 · Cited by 32 · cited 1× · see citing works
“One implementation of FireFlies, entitled ‘ClassBeacons’, visualizes teacher proximity [63, 80]: how the teacher divides time and attention over students during a class session.”
BackgroundVanLehn, K. A., Burkhardt, H., Cheema, S., Kang, S., Pead, D., Schoenfeld, A. H., & Wetzel, J. (2019) · Interactive Learning Environments · doi:10.1080/10494820.2019.1616567 · Cited by 30 · cited 7× · see citing works
“, by helping teachers dynamically transition students between individual and group activities, or enabling them to discretely communicate with students during class via a tablet interface [30, 52, 94, 125].”
BackgroundMethodInfluentialUse of Dashboards and Visualization Techniques to Support Teacher Decision Making
Mottus, A., Kinshuk, K., Sabine Graf, A., & Chen, N. (2014) · Lecture notes in educational technology · doi:10.1007/978-3-662-44659-1_10 · Cited by 29 · see citing works
LOCO-Analyst: A Tool for Raising Teachers’ Awareness in Online Learning Environments
Jovanović, J., Gašević, D., Brooks, C. A., Devedžić, V., & Hatala, M. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-75195-3_9 · Cited by 28 · cited 3× · see citing works
“Furthermore, TA tools have been designed to support a range of teacher processes, many of which are covered by the notion of classroom orchestration [30, 71, 125], such as monitoring [61, 107, 118, 122] and decision-making [12, 90, 92, 135].”
BackgroundMethodReal-time visualization of student activities to support classroom orchestration
Tissenbaum, M., Matuk, C. F., Berland, M., et al. (2016) · International Conference of Learning Sciences · doi:10.22318/icls2016.175 · Cited by 28 · cited 3× · see citing works
“TA systems have been developed across a range of disciplines, including HCI [11, 12, 66], learning analytics [83, 86, 127], teacher education [58, 111, 112], AI in education [40, 55, 125, 141], and the learning sciences [71, 122].”
BackgroundReframing classroom sensing: promise and peril
Ogan, A. (2019) · Interactions · doi:10.1145/3358902 · Cited by 28 · see citing works
Knowing by ear: leveraging human attention abilities in interaction design
Bakker, S., van den Hoven, E., & Eggen, B. H. (2011) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-011-0062-8 · Cited by 26 · cited 1× · see citing works
“Therefore, distributing supportive information over multiple sensory channels could further enable teachers’ unobtrusive perception of the information [9, 17].”
BackgroundSelf-citationTinkerLamp 2.0: Designing and Evaluating Orchestration Technologies for the Classroom
Do-Lenh, S., Jermann, P., Legge, A., Zufferey, G., & Dillenbourg, P. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33263-0_6 · Cited by 26 · see citing works
Levels of interaction and proximity: Content analysis of video-based classroom cases
Kale, U. (2008) · Internet and Higher Education · doi:10.1016/j.iheduc.2008.06.004 · Cited by 26 · see citing works
van Alphen, E., & Bakker, S. (2016) · doi:10.1145/2851581.2892524 · Cited by 26 · cited 4× · see citing works
“Lernanto [7], for example, uses wall-mounted, arrayed LEDs to depict each student’s learning pace [6], which provides teachers with background awareness that can help them in differentiating instruction accordingly.”
BackgroundInfluentialSelf-citationFreedom to Personalize My Digital Classroom
Vermette, L., McGrenere, J., Birge, C., Kelly, A., & Chilana, P. K. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300548 · Cited by 25 · cited 2× · see citing works
“Other work has proposed design implications or conceptual frameworks for smart classrooms [44, 128], orchestration systems [31, 100], learning analytics dashboards [126], Open Learner Models [23], or reflection-aiding tools [10, 66, 120].”
BackgroundKerry, T., & Kerry, C. (1997) · Educational Studies · doi:10.1080/0305569970230309 · Cited by 24 · cited 1× · see citing works
“For instance, while teaching, a teacher has to continuously observe each learner’s current state, in order to reflectively decide which learners are in need of support and what types of support should be offered [65, 124].”
BackgroundKharrufa, A., Rix, S., Osadchiy, T., Preston, A., & Olivier, P. (2017) · International Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025679 · Cited by 21 · cited 10× · see citing works
“Yet the communication of such knowledge has proven fruitful in various HCI domains [56, 83, 130, 133]: for instance, in helping designers and researchers to explicate crucial yet implicit design tensions or trade-offs, or explore relevant but less-charted regions of the design space.”
BackgroundMethodInfluentialInforming the Design of Teacher Awareness Tools through Causal Alignment Analysis.
Holstein, K., McLaren, B. M., & Aleven, V. (2018) · International Society of the Learning Sciences, Inc. [ISLS]. eBooks · doi:10.22318/cscl2018.104 · Cited by 20 · cited 2× · see citing works
“What to notice: Teacher awareness tools [53, 83, 107] are commonly designed to augment teachers’ perceptions of what goes on in their classrooms.”
BackgroundMethodSelf-citationAguilar, S. J. (2017) · Journal of Research on Technology in Education · doi:10.1080/15391523.2017.1401498 · Cited by 20 · cited 2× · see citing works
“…pointed to the use of spatially-distributed displays, the participating teachers were uncomfortable with information being public as in the FireFlies system, noting that some of the information most useful for their own decision-making could be stigmatizing if shared with the entire class [1, 50].”
BackgroundUnderstanding teachers’ routines to inform classroom technology design
An, P., Bakker, S., & Eggen, B. H. (2016) · Education and Information Technologies · doi:10.1007/s10639-016-9494-9 · Cited by 20 · see citing works
Self-citationQuintana, R. M., Quintana, C., Madeira, C. A., & Slotta, J. D. (2016) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2851581.2892493 · Cited by 20 · see citing works
Lui, M., Kuhn, A., Acosta, A., Quintana, C., & Slotta, J. D. (2014) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557162 · Cited by 19 · cited 1× · see citing works
“At the intersection of HCI and education, research has predominantly focused on the design of technologies to support learners (e.g., [3, 8, 43, 75, 78, 121]).”
BackgroundVerweij, D., Bakker, S., & Eggen, B. H. (2017) · ACM International Conference on Interactive Surfaces and Spaces (ISS) · doi:10.1145/3132272.3134122 · Cited by 16 · cited 7× · see citing works
“Two implementations of FireFlies platform [129]: (Left) ‘ClassBeacons’ [10] and (Right) ‘FireFlies-VLE’ [12].”
BackgroundMethodInfluentialSelf-citationDeveloping a novel interface for capturing self reports of affect
Alsmeyer, M., Luckin, R., & Good, J. (2008) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1358628.1358778 · Cited by 16 · cited 1× · see citing works
“At the intersection of HCI and education, research has predominantly focused on the design of technologies to support learners (e.g., [3, 8, 43, 75, 78, 121]).”
Backgroundd’Anjou, B., Bakker, S., An, P., & Bekker, T. M. (2019) · Designing Interactive Systems Conference · doi:10.1145/3322276.3322365 · Cited by 16 · cited 10× · see citing works
“Two implementations of FireFlies platform [129] : (Left) ‘ClassBeacons’ [10] and (Right) ‘FireFlies-VLE’ [12] .”
BackgroundMethodInfluentialSelf-citationAffective transfer computing model based on attenuation emotion mechanism
Shi, Z., Wei, J., Wang, Z., Tu, J., & Zhang, Q. (2011) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-011-0071-7 · Cited by 14 · see citing works
Students' use of mobile technology to collect data in guided inquiry on field trips
Lo, W.-T., & Quintana, C. (2013) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/2485760.2485837 · Cited by 13 · cited 1× · see citing works
“At the intersection of HCI and education, research has predominantly focused on the design of technologies to support learners (e.g., [3, 8, 43, 75, 78, 121]).”
BackgroundHoorn, J. F. (2005) · Cognition Technology & Work · doi:10.1007/s10111-004-0172-0 · Cited by 13 · see citing works
Towards a Framework for Smart Classrooms that Teach Instructors to Teach
Gerritsen, D., Zimmerman, J., & Ogan, A. (2018) · International Conference of Learning Sciences · Cited by 13 · see citing works
GlassClass: Exploring the Design, Implementation, and Acceptance of Google Glass in the Classroom
Berque, D. A., & Newman, J. T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21067-4_25 · Cited by 12 · see citing works
Intermediate-level knowledge in child-computer interaction
Barendregt, W., Bekker, T. M., Börjesson, P., et al. (2018) · doi:10.1145/3202185.3205865 · Cited by 12 · cited 1× · see citing works
“Such (and many other kinds of) intermediate-level knowledge is still scarce in educational technology research [18, 79, 99].”
BackgroundDefining, designing, and evaluating peripheral displays
MatthewsTara, RattenburyTye, & CarterScott (2007) · Human-Computer Interaction · doi:10.5555/1466595.1466602 · Cited by 11 · see citing works
Gennari, R., Melonio, A., & Rizvi, M. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1614226 · Cited by 10 · cited 2× · see citing works
“, [3, 8, 43, 75, 78, 121]).”
Evidence, Efficacy, and Effectiveness
Cochran‐Smith, M. (2005) · Journal of Teacher Education · doi:10.1177/0022487105284709 · Cited by 9 · see citing works
van Leeuwen, A., Rummel, N., Aleven, V., Gal, K., Holstein, K., Campen, C. A. N. K., McLaren, B. M., Molenaar, I., Prusak, N., Schwarz, B. B., et al. (2018) · International Conference of Learning Sciences · doi:10.22318/cscl2018.1227 · Cited by 9 · see citing works
Self-citationUnselfconscious interaction: a conceptual construct. Interacting with Computers
Wakkary, R., Desjardins, A., & Hauser, S. (2015) · Summit (Simon Fraser University) · Cited by 8 · see citing works
Effects of synchronous coaching in teacher training
Hooreman, R. W., Kommers, P., & Jochems, W. M. G. (2008) · International Journal of Continuing Engineering Education and Life-Long Learning · doi:10.1504/ijceell.2008.018836 · Cited by 8 · cited 9× · see citing works
“, ambient sound notifications [51] and live coaching via an earpiece [58]).”
BackgroundInfluentialTowards Integrating Human and Automated Tutoring Systems.
RITTER, S., Yudelson, M. V., Fancsali, S. E., & Berman, S. (2016) · Educational Data Mining · Cited by 7 · see citing works
Strong Technology-Enhanced Learning Concepts
Prieto, L. P., Alavi, H. S., & Verma, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66610-5_42 · Cited by 6 · cited 2× · see citing works
“rest in real-time teaching augmentation across the aforementioned disciplines, these diverse design solutions have not previously been examined together to generalize more fundamental design insights [57, 79, 99]. Prior work has introduced -oriented process frameworks to guide the iterative design of particular kinds…”
BackgroundLernanto: An ambient display to support differentiated instruction
van Alphen, E., & Bakker, S. (2015) · TU/e Research Portal · Cited by 5 · see citing works
Self-citationNikolayeva, I., Martin, B., Yessad, A., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-98572-5_53 · Cited by 4 · cited 2× · see citing works
“Furthermore, TA tools have been designed to support a range of teacher processes, many of which are covered by the notion of classroom orchestration [30, 71, 125], such as monitoring [61, 107, 118, 122] and decision-making [12, 90, 92, 135].”
BackgroundHow did the e-learning session go? The student inspector
Scheu, O., & Zinn, C. (2007) · Max Planck Digital Library · Cited by 4 · see citing works
Olsen, J. K., Rummel, N., & Aleven, V. (2018) · Cited by 4 · see citing works
ClassBeacons: designing distributed visualization of teachers’ physical proximity in the classroom.
An, P. P., Bakker, S. S., Ordanovski, S., et al. (2018) · TU/e Research Portal · Cited by 3 · see citing works
Self-citationTissenbaum, M., & Slotta, J. D. (2014) · International Conference of Learning Sciences · Cited by 2 · see citing works
Bakker, S. S., van den Hoven, E. E., & Eggen, J. B. (2013) · TU/e Research Portal · Cited by 2 · see citing works
Self-citation
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Research works printed but not linked: 15
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A Conceptual Framework for Human–AI Hybrid Adaptivity in Education
Holstein, K., Aleven, V., & Rummel, N. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-52237-7_20 · Cited by 168 · see citing works
“…some technologies are designed to present information to humans with low-level, minimally pre-interpreted data (e.g., “number of help requests”) [4, 5, 16], several of the AIEd systems discussed above, including APTA , the Help Tutor , and Lumilo rely upon advanced student modeling techniques…”
BackgroundInfluentialSelf-citationDesigning for human–AI complementarity in K‐12 education
Holstein, K., & Aleven, V. (2022) · AI Magazine · doi:10.1002/aaai.12058 · Cited by 156 · see citing works
“…to be very important to teachers, who would often use the examples to second guess the system’s judgments and try to infer deeper underlying causes of student difficulties—an example of one form of complementarity between the AI and the teacher (An et al. 2020; Holstein, McLaren, and Aleven 2019a).”
BackgroundMethodInfluentialSelf-citationExploring Immersive Learning Experiences: A Survey
Kuhail, M. A., ElSayary, A., Farooq, S., & Alghamdi, A. (2022) · Informatics · doi:10.3390/informatics9040075 · Cited by 131 · see citing works
“A notable recent effort in this direction is a framework devised by An et al. [140], assisting K-12 educators with the design and analysis of teaching augmentation.”
MethodNazaretsky, T., Bar, C., Walter, M., & Alexandron, G. (2022) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3506860.3506861 · Cited by 86 · see citing works
“, educational dashboards, teaching augmentation tools) were designed within this conceptual framework (see for example [3, 43]).”
MethodSeo, K., Yoo, M., Dodson, S., & Jin, S.-H. (2024) · Journal of Research on Technology in Education · doi:10.1080/15391523.2024.2330525 · Cited by 95 · see citing works
“The design of AI-based teacher support systems is often driven more by the availability of extant technical solutions than a comprehensive analysis of teachers’ needs or expectations (An et al., 2020).”
Human-Centered Design Principles for Actionable Learning Analytics
Dimitriadis, Y. A., Martínez‐Maldonado, R., & Wiley, K. (2020) · Springer eBooks · doi:10.1007/978-3-030-64363-8_15 · Cited by 70 · see citing works
“Eventually teacher augmentation might be pursued to bring such balance (An et al., 2020), since scholar design knowledge can be embedded in tools, and can complement both the tacit and explicit design knowledge of teachers,”
BackgroundWhat Do You Mean by Collaboration Analytics? A Conceptual Model
Martínez‐Maldonado, R., Gašević, D., Echeverría, V., Fernandez-Nieto, G. M., Swiecki, Z., & Shum, S. B. (2021) · Journal of Learning Analytics · doi:10.18608/jla.2021.7227 · Cited by 70 · see citing works
Yang, K. B., Echeverria, V., Lu, Z., Mao, H., Holstein, K., Rummel, N., & Aleven, V. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581398 · Cited by 47 · see citing works
“Various educational tools have been designed and developed to support teaching and learning processes mediated by technology [5, 27].”
BackgroundInfluentialSelf-citationParticipatory Design Goes to School: Co-Teaching as a Form of Co-Design for Educational Technology
Nicholson, R., Bartindale, T., Kharrufa, A., Kirk, D. S., & Walker‐Gleaves, C. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517667 · Cited by 46 · see citing works
Kim, J., Ham, Y., & Lee, S.-S. (2024) · Australasian Journal of Educational Technology · doi:10.14742/ajet.8859 · Cited by 54 · see citing works
“AI systems enhance students’ sensing capacities and may also support students in reflecting on and interpreting the information they receive through the system so that they may effectively mediate and make sense of the AI’s interpretations (An et al., 2020).”
BackgroundA Deep Transfer Learning Approach to Modeling Teacher Discourse in the Classroom
Jensen, E., Pugh, S. L., & D’Mello, S. K. (2021) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3448139.3448168 · Cited by 44 · see citing works
“For systems related to student learning, analytics often centers on information like student engagement [5] or if a student needs immediate assistance [3, 38].”
BackgroundHolstein, K., De‐Arteaga, M., Tumati, L., & Cheng, Y. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579628 · Cited by 39 · see citing works
“AI-based decision support tools (ADS) are being used to augment human decision-making across a growing range of domains—from predictive risk models used to augment decision-making in public services [11, 25, 27, 34, 41], to AI-based teacher support tools used in K-12 education [2, 7, 13, 23, 40], to clinical decision…”
BackgroundMethodSelf-citationEarly Prediction of Visitor Engagement in Science Museums with Multimodal Learning Analytics
Emerson, A., Henderson, N., Rowe, J., Min, W., Lee, S., Minogue, J., & Lester, J. C. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3418890 · Cited by 31 · see citing works
“identifying learners’ collaborative behaviors in learning contexts [18, 25, 40], assisting teachers with classroom orchestration [1, 2, 36], and understanding visitor engagement in science museums [19].”
BackgroundHolstein, K., & Doroudi, S. (2021) · arXiv · doi:10.48550/arxiv.2104.12920 · Cited by 29 · see citing works
Self-citationA Dashboard to Support Teachers During Students’ Self-paced AI-Supported Problem-Solving Practice
Aleven, V., Blankestijn, J., Lawrence, L., Nagashima, T., & Taatgen, N. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-16290-9_2 · Cited by 28 · see citing works
“This work has resulted in novel tools and insight into how best to design these kinds of tools [1, 2, 4, 9, 13, 14, 16, 25].”
BackgroundPossaghi, I., Vesin, B., Zhang, F.-R., Sharma, K., Knudsen, C., Bjørkum, H., & Papavlasopoulou, S. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00410-4 · Cited by 27 · see citing works
“This position aligns with An et al. (2020) work on the delicate balance between teacher autonomy and orchestration automation, where over-reliance on computed outcomes risks reducing a teacher’s role to that of a mere executor (An et al., 2020).”
InfluentialKasepalu, R., Chejara, P., Prieto, L. P., & Ley, T. (2021) · Technology Knowledge and Learning · doi:10.1007/s10758-021-09522-5 · Cited by 22 · see citing works
“The teaching augmentation framework suggests that teachers might need more scaffolding from the dashboard for the data to be actionable as too low-level data does not instigate an intervention (An et al., 2020).”
BackgroundInfluentialResponsible AI in Education: Understanding Teachers’ Priorities and Contextual Challenges
Yin, Y., Karumbaiah, S., & Acquaye, S. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732176 · Cited by 21 · see citing works
Yang, K. B., Zhou, X., & Radu, I. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13779 · Cited by 20 · see citing works
“The general review method of this paper was inspired by a review on teacher augmentation systems [40].”
MethodReplay Enactments: Exploring Possible Futures through Historical Data
Holstein, K., Harpstead, E., Gulotta, R., & Forlizzi, J. (2020) · Proceedings of the 2020 ACM Designing Interactive Systems Conference · doi:10.1145/3357236.3395427 · Cited by 19 · see citing works
“As a teacher walks throughout the room while wearing Lumilo , they can see real-time indicators about students’ learning, metacognitive, and behavioral states, floating directly above students’ heads [2, 17].”
BackgroundSelf-citationInterest in Educational Data and Barriers to Data Use Among Massive Open Online Course Instructors
Usher, M., & Hershkovitz, A. (2022) · Journal of Science Education and Technology · doi:10.1007/s10956-022-09984-x · Cited by 18 · see citing works
“This observation has led recent studies to explore new methods and strategies for co-designing LA tools with critical stake-holders, such as the teachers (An et al., 2020; Holstein et al., 2019).”
BackgroundMethodImproving Automated Scoring of Student Open Responses in Mathematics
Baral, S., Botelho, A. F., Erickson, J. A., Benachamardi, P., & Heffernan, N. T. (2021) · Educational Data Mining · Cited by 17 · see citing works
Merikko, J., & Kivimäki, V. (2022) · Frontiers in Education · doi:10.3389/feduc.2022.1010255 · Cited by 17 · see citing works
“While teachers are considered to have a low risk of being automated (Frey and Osborne, 2017), augmenting teachers’ pedagogical abilities (An et al., 2020) seems like a more likely scenario than complete automation of teaching.”
BackgroundMultimodal Predictive Student Modeling with Multi-Task Transfer Learning
Emerson, A., Min, W., Rowe, J., Azevedo, R., & Lester, J. C. (2023) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3576050.3576101 · Cited by 14 · see citing works
“Multimodal data have been used to model student engagement [18, 52] and knowledge [8, 24, 43], and have been utilized to support teachers in classrooms [1, 2, 38].”
MethodSLADE: A Method for Designing Human-Centred Learning Analytics Systems
Alfredo, R., Echeverría, V., Jin, Y., Swiecki, Z., Gašević, D., & Martínez‐Maldonado, R. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636847 · Cited by 14 · see citing works
“This is a generic seed (divergent thinking) design step that is common among other LA design approaches (e.g., [3, 30, 52]), and it is aimed at helping participants generate diverse, and potentially divergent, ideas of LA systems that may improve the learning experience.”
BackgroundEvaluation of teachers’ orchestration tools usage in robotic classrooms
Shahmoradi, S., Kothiyal, A., Bruno, B., & Dillenbourg, P. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11909-z · Cited by 13 · see citing works
“To characterize these tools, Holstein et al have proposed a framework, named Teaching Augmentation (TA) framework (An et al., 2020), which proposes a five-dimensional design space for orchestration tools: the first three dimensions describe the range of how, when and to what extent the…”
Design Principles for Teacher Dashboards to Support In-Class Learning
Poh, A., Castro, F. E. V., & Arroyo, I. (2023) · Proceedings. · doi:10.22318/icls2023.197114 · Cited by 10 · see citing works
How Peripheral Interactive Systems Can Support Teachers with Differentiated Instruction
Sellier, N., & An, P. (2020) · Designing Interactive Systems Conference · doi:10.1145/3357236.3395497 · Cited by 14 · see citing works
“Various forms of technologies that aimed for classroom orchestration [21], or teaching augmentation [7], can benefit teachers’ classroom differentiation.”
BackgroundSelf-citationSocial sharing of task-related emotions in Design-Based Learning: Challenges and opportunities
Zhang, F., Markopoulos, P., An, P., & Schüll, M. (2021) · Int. J. Child Comput. Interact. · doi:10.1016/j.ijcci.2021.100378 · Cited by 8 · see citing works
Self-citationMerikko, J., Ng, K., Saqr, M., & Ihantola, P. (2022) · IEEE Access · doi:10.1109/access.2022.3207274 · Cited by 9 · see citing works
Educational Data Mining and Learning Analytics
Khine, M. S. (2024) · Springer eBooks · doi:10.1007/978-981-97-9350-1_1 · Cited by 8 · see citing works
A Survey of K-12 Teacher Needs for an Online Programming Learning System
Limke, A., Hill, M., Cateté, V., et al. (2024) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3651110 · Cited by 7 · see citing works
A Design Space for Intelligent Dialogue Augmentation
Chan, R. S. M., Marx, A., Kim, A., & El‐Assady, M. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712096 · Cited by 6 · see citing works
“As such, An et al. [6] de-fnes design considerations regarding the user interface and system initiation for language processing systems to enhance classroom teaching.”
Ramesh, K., Vermette, L., & Chilana, P. K. (2021) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3430895.3460133 · Cited by 6 · see citing works
“A variety of novel technologies aim to help teachers manage their classroom effectively [1,2,14] and to support the creation of impactful learning experiences within an LMS [7].”
BackgroundJiang, G., Zhu, J., Li, Y., An, P., & Wang, Y. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12059-y · Cited by 6 · see citing works
“According to the design dimensions in the TA framework (An et al., 2020 ), we tend to build a low-attentional (for both teachers and students), low-social visibility (only teachers see the UI), and real-time system that accurately recognizes students in a large classroom and minimizes interruption…”
InfluentialSelf-citationHuman-centered Approaches to Data-informed Feedback
Tsai, Y., & Martínez‐Maldonado, R. (2022) · Solar eBooks · doi:10.18608/hla22.021 · Cited by 5 · see citing works
Scianna, J., & Kaliisa, R. (2023) · Educational Technology Research and Development · doi:10.1007/s11423-023-10310-4 · Cited by 5 · see citing works
“…methodology provide an example of how technology developers can design tools integrating multiple dimensions of student learning experiences and contributions to create robust visualizations that enhance teachers’ in-the-moment awareness, a focus of the AI in Education community (An et al., 2020).”
Empowering Students as Leaders of Co-Design for Block-Based Programming
Limke, A., Lytle, N., Lin, M., et al. (2023) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3585775 · Cited by 5 · see citing works
“…has shown a range of considerations that must go into learning technologies in order to support teachers, including persevering their autonomy as teachers [2], supporting different classroom formats [28], maintenance of hardware [27] and mappings of learning environment content to curricula [28].”
BackgroundYin, Y., Karumbaiah, S., Lim, J., Acquaye, S., & Schwartz, S. (2025) · Computer-supported collaborative learning/The Computer-Supported Collaborative Learning Conference · doi:10.22318/cscl2025.817482 · Cited by 4 · see citing works
“Teachers ’ pedagogical commitments and the diverse use cases of AI—such as grading (Boe et al., 2013), scaffolding science learning (Perez-Alvarez et al., 2022; Persico & Pozzi, 2015), and classroom management (An et al., 2020; Brunskill et al., 2024)—further complicate the landscape.”
Human Tutoring Improves the Impact of AI Tutor Use on Learning Outcomes
Gurung, A., Lin, J., Gutterman, J., Thomas, D. R., Houk, A., Gupta, S., Brunskill, E., Branstetter, L., Aleven, V., & Koedinger, K. R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98459-4_28 · Cited by 4 · see citing works
Comparing Visualizations to Help a Teacher Effectively Monitor Students in a VR Classroom
Rahman, Y., Kulshreshth, A. K., & Borst, C. W. (2023) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar59233.2023.00059 · Cited by 4 · see citing works
“These tools are of various types including but not limited to ambient displays, wearables, or learning analytics dashboards [5].”
BackgroundExploring the Role of AI in K12
Hossain, S. F. A., Nurunnabi, M., Nadi, A. H., Hasan, A. M., & Ahsan, F. T. (2021) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-7998-6480-6.ch008 · Cited by 4 · see citing works
CalMe: A Tangible Environment to Enhance Pupils Group Work Regulation
Bertolo, D., Faedda, S., Olry, A., Veytizou, J., Vivian, R., & Fleck, S. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3685273 · Cited by 3 · see citing works
“An analysis of VoluMe according to the five dimensions of An et al.’s TA Framework [4] may provide some avenues for improving the artifact.”
Grand Challenges for Pervasive Technology to Transform Pervasive Education
Kay, J. (2022) · IEEE Pervasive Computing · doi:10.1109/mprv.2022.3199104 · Cited by 3 · see citing works
Examining the Use of an AI-Powered Teacher Orchestration Tool at Scale
Brunskill, E., Norberg, K. A., Fancsali, S. E., & Ritter, S. (2024) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3657604.3664704 · Cited by 3 · see citing works
“Increased adoption of educational technology has brought increased opportunity for artificial intelligence tools that support teacher-student orchestration during live interactions (see recent reviews [2, 5]).”
Exploring Information Retrieval for Personalized Teaching Support
Rao, N., Chu, S. L., Jing, Z., et al. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-19682-9_25 · Cited by 3 · see citing works
Vermette, L., Ramesh, K., McGrenere, J., & Chilana, P. K. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501846 · Cited by 2 · see citing works
“propose a broader framework to support the design of “teaching augmentation” tools [2], such as those that provide educators with new personalizable hardware or software capabilities to rely on during their teaching routines (e.”
BackgroundElementary and Secondary School Teachers’ Perceptions of Learning Analytics: A Qualitative Approach
Valtonen, T., Paavilainen, T., López‐Pernas, S., Saqr, M., & Hirsto, L. (2025) · Technology Knowledge and Learning · doi:10.1007/s10758-025-09847-5 · Cited by 3 · see citing works
“This aligns with An et al. (2020), who explain that dashboards are developed to ‘extend teachers’ in-the-moment awareness in blended class-rooms’ (p. 2).”
Zhang, L., Park, J. Y., Menon, N. M., Ranade, N., Yu, B., & Tan, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94153-5_44 · Cited by 2 · see citing works
Rahman, Y., Kulshreshth, A. K., & Borst, C. W. (2023) · IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · doi:10.1109/vrw58643.2023.00192 · Cited by 2 · see citing works
“These applications are of various types including but not limited to ambient displays, wearables, or learning analytics dashboards [1].”
BackgroundIslam, M. A., Varghese, D., Richardson, D., Saputra, M. R. U., Saha, M., Bartindale, T., Mukta, M. S. H., Kaniaswari, R., Yousuf, M. A. H., & Olivier, P. L. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757458 · Cited by 1 · see citing works
“This segment has potential to enhance communities’ active learning and creativity, as well as supporting them for private or sensitive discussions, as emphasized particularly in pedagogy [2, 88].”
Kong, M., Mauriello, M. L., & Pollock, L. (2022) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3564721.3564725 · Cited by 1 · see citing works
“In the second part of the interview, teachers were asked questions about their preferences in a potential Scratch TA system design, based on the TA framework described in Section 3 [13].”
BackgroundMethodInfluentialTangiTeam: Supporting Social Regulation of Learning during Design-Based Learning
Veldhuis, A., Antle, A. N., & Riecke, B. E. (2023) · International Conference on Tangible, Embedded, and Embodied Interaction (TEI) · doi:10.1145/3569009.3573111 · Cited by 1 · see citing works
“A hybrid system of the FireFlies together with wireless scanners and a dashboard could support teachers to acquire multiple levels of information [2] and give diferentiated instruction [30].”
BackgroundJiang, G., Li, J., Ma, X., & Luo, Q. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789094
ProjecTA: A Semi-Humanoid Robotic Teaching Assistant with In-Situ Projection for Guided Tours
Zhou, H., Zhang, Y., Zhang, Z., Zhang, W., Wang, C., & An, P. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790577
“For instance, Im-proVisAR overlays a ‘piano roll’ onto a keyboard as intuitive guidance [30], while LuminAR turns a desk into an interactive surface [61].”
Self-citationEvaluAId: Human-AI Collaborative Evaluation of Open-Ended Student Essays
Zhang, C., Ju, K. P., Lu, X., Yen, Y.-C. G., & Rzeszotarski, J. M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790814
“In this line of work, prior studies have explored human–AI approaches across a wide range of contexts for supporting teaching [4, 50].”
Explorations of Designing Spatial Classroom Analytics with Virtual Prototyping
Jang, J., Lee, J., Echeverría, V., et al. (2021) · International Learning Analytics and Knowledge Conference (LAK) · doi:10.1145/3448139.3448192
“The resulting analyses can provide meaningful feedback to teachers by making teacherstudents’ interactions visible [1, 5, 17], improving teachers’ awareness and directing attention to struggling students [4, 12, 13], or giving early alerts about students’ progress and engagement [5, 9, 13].”
BackgroundDiscovering the Role of Artificial Intelligence in K12
Nadi, A. H., Hossain, S. F. A., Safin, M. A., Sofin, M. R., & Akhter, R. (2021) · Advances in mobile and distance learning book series · doi:10.4018/978-1-7998-6829-3.ch018
Framework Design of Expression Concentration Feedback System in the Online Classroom
Zhou, X., Gong, S., & Ying, W. (2023) · Atlantis Highlights in Computer Sciences/Atlantis highlights in computer sciences · doi:10.2991/978-94-6463-172-2_109
Methodological Analysis of Machine Learning Courses for Elementary to High School Students
Parraguez, E. A. V., Loayza, P. C., Carrillo, M. D. J. M., Noblega, H., Cabrera, F. A. L., Sardón-Quispe, W. W., Pichón, F. M. A., Luján, H. E. V., Lagos, J. C. E., & Panduro, B. C. (2023) · Journal of Namibian Studies History Politics Culture · doi:10.59670/jns.v34i.1407
“One study examined the ML process with children in a household setting, whereas others, including (An et al., 2020), promoted AI literacy in museum-like settings and online.”
BackgroundNezhad, M. H., Castro, F. E. V., Woolf, B. P., & Arroyo, I. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72315-5_21
Liao, J., Yang, Z., Wu, Q., et al. (2025) · International Conference on Education and Training Technologies (ICETT) · doi:10.1109/icett66247.2025.11137181
“Teaching Augmentation (TA) system extends and supplements teachers' instructional abilities during ongoing classroom activities [3].”
Adaptive teaching in higher education: real-time design patterns for educator-led instruction
Alajlani, N., & Crabb, M. (2025) · Journal of Computing in Higher Education · doi:10.1007/s12528-025-09487-9
Tan, P., Guo, Z., Ji, Y., et al. (2026) · ACM Transactions on Computing Education · doi:10.1145/3813112
“Three main areas of related work influence our work in investigating a novel game design education: (1) frameworks and challenges in game design education, (2) understanding rhythm game design, and (3) approaching synergized interactions in rhythm game design.”
SMART CLASSROOM: TAXONOMY OF FEATURES, EXISTING TECHNIQUES, CHALLENGES AND RESEARCH OPPORTUNITIES
Timothy, M., & Sadiq, S. (2026) · Nigerian Journal of Technology · doi:10.4314/njt.v45i1.10
Using an MR-Based Teacher Orchestration Tool in AI-Supported K-12 Classrooms
Jin, Q., Zhan, Y., Morgus, W., Price, K., Sandbothe, M., Sewall, J., Popescu, O., Berman, S., Fancsali, S. E., RITTER, S., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29770-9_36
Self-citationWhen AI Breaks, Teachers Repair: Pedagogical Repair Work in Situated Classroom Practice
Yin, Y., Karumbaiah, S., Kim, Y., & Saxena, D. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812281
“…intelligence (AI) is increasingly promoted as a solution for advancing teaching and learning [38, 54], with tools being introduced for a wide range of classroom purposes, including scaffolding science learning [44, 46], automated assessment [15, 42], and classroom management [2, 7, 12, 66, 68].”
From Eye Movements to Instructional Insights: Co-designing an AI Dashboard with Teachers
Tsiakas, K., Catrysse, L., Buseyne, S., van Daal, T., Donche, V., Gijbels, D., & Jarodzka, H. M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37982-5_37
Lyu, Q., Alonso-Prieto, V., Jin, Q., Morgus, W., Price, K., Fancsali, S. E., Berman, S. R., RITTER, S., & Aleven, V. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37979-5_28
- Authors
- An, P., Holstein, K., d'Anjou, B., Eggen, B., & Bakker, S.
- Affiliations
- Eindhoven University of Technology (NL)
- Carnegie Mellon University (US)
- Eindhoven University of Technology, Eindhoven, Netherlands
- Philips Experience Design, Eindhoven, Netherlands
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-17
- OpenAlex
- W2999030356
- Semantic Scholar
- f8f2c3250ffa93103ffc7fec9763fafc5a1838ac
- Cited by
- 69
- References
- 155
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 8 · 2023: 13 · 2022: 13 · 2021: 10 · 2020: 6
- FWCI
- 3.24
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- K-12 classrooms, design space, design trade-offs, teacher professional autonomy, unobtrusiveness, ambient displays, wearable devices, learning analytics dashboards, pedagogical support tools
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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