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Will Computers Replace School Psychologists? An Analysis of Tech-Based Tools for Assessment, Consultation, and Counseling

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Abstract

The field of school psychology has seen the gradual implementation of technology in day-to-day practice. The earliest computer-based technology adopted by school psychologists consisted of software programs used to score tests, run analyses for multi-tiered systems of support, and aid in tele-consultation. These tasks have one thing in common; they require a human to drive the computer-based activity. The use of technology independent of human support is in its nascency among school psychologists, but the development of these types of tools continues at a rapid pace. This conceptual paper discusses self-driven technological options, both current and developing, for school psychologists to use in assessment, consultation, and counseling. Such technological tools come with their own problems, but they also offer unique benefits previously unavailable to school psychologists. This paper discusses the pros and cons of using these tools. In addition, the author describes the need for additional research as well as updated ethical guidelines related to the integration of technology into the practice of school psychology.

Impact Statement

This conceptual paper examines the established paradigm that only humans can deliver school psychology services. The work offers predictions for school psychologists as to which aspects of the job are most and least replaceable by self-driven technology.

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References

  • Al-Fudail, M., & Mellar, H. (2008). Investigating teacher stress when using technology. Computers & Education, 51(3), 1103–1110. https://doi.org/10.1016/j.compedu.2007.11.004

    Article  Google Scholar 

  • Alonzo, J., Tindal, G., Ulmer, K., & Glasgow, A. (2006). easyCBM online progress monitoring assessment system. Center for Educational Assessment Accountability http://easycbm.com

  • Assessment Technologies. (2020). MEZURE clinical manual. Author.

  • Ayata, D., Yaslan, Y., & Kamaşak, M. (2017). Emotion recognition via galvanic skin response: Comparison of machine learning algorithms and feature extraction methods. Istanbul University-Journal of Electrical & Electronics Engineering, 17(1), 3147–3156.

    Google Scholar 

  • Bagby, R. M., & Sellbom, M. (2018). The validity and clinical utility of the personality inventory for DSM–5 response inconsistency scale. Journal of Personality Assessment, 100(4), 398–405. https://doi.org/10.1080/00223891.2017.1420659

    Article  PubMed  Google Scholar 

  • Benjamin, A. (2014). Differentiated instruction using technology: A guide for middle & high school teachers. Routledge.

    Book  Google Scholar 

  • Benson, N. F., Floyd, R. G., Kranzler, J. H., Eckert, T. L., Fefer, S. A., & Morgan, G. B. (2019). Test use and assessment practices of school psychologists in the United States: Findings from the 2017 national survey. Journal of School Psychology, 72, 29–48. https://doi.org/10.1016/j.jsp.2018.12.004

    Article  PubMed  Google Scholar 

  • Bergan, J. R. (1977). Behavioral consultation. Merrill.

    Google Scholar 

  • Boden, M. A. (1996). Creativity. In M. A. Boden (Ed.), Handbook of perception and cognition: Artificial intelligence (pp. 267–291). Academic Press.

    Google Scholar 

  • Booth-Kewley, S., Larson, G. E., & Miyoshi, D. K. (2007). Social desirability effects on computerized and paper-and-pencil questionnaires. Computers in Human Behavior, 23(1), 463–477. https://doi.org/10.1016/j.chb.2004.10.020

    Article  Google Scholar 

  • Bramlett, R. K., Murphy, J. J., Johnson, J., Wallingsford, L., & Hall, J. D. (2002). Contemporary practices in school psychology: A national survey of roles and referral problems. Psychology in the Schools, 39(3), 327–335. https://doi.org/10.1002/pits.10022

    Article  Google Scholar 

  • Brown, D., Pryzwansky, W. B., & Schulte, A. C. (2001). Psychological consultation: Introduction to theory and practice (5th ed.). Allyn & Bacon.

    Google Scholar 

  • Butcher, J. N., Perry, J. N., & Atlis, M. M. (2000). Validity and utility of computer-based test interpretation. Psychological Assessment, 12(1), 6–18. https://doi.org/10.1037/1040-3590.12.1.6

    Article  PubMed  Google Scholar 

  • Caplan, G. (1970). The theory and practice of mental health consultation. Basic Books.

    Google Scholar 

  • Castillo, J. M., Curtis, M. J., & Tan, S. Y. (2014). Personnel needs in school psychology: A 10-year follow-up study on predicted personnel shortages. Psychology in the Schools, 51(8), 832–849. https://doi.org/10.1002/pits.21786

    Article  Google Scholar 

  • Cernich, A. N., Brennana, D. M., Barker, L. M., & Bleiberg, J. (2007). Sources of error in computerized neuropsychological assessment. Archives of Clinical Neuropsychology, 22(Supp 1), S39–S48. https://doi.org/10.1016/j.acn.2006.10.004

    Article  PubMed  Google Scholar 

  • Clark, A. J. (2014). Empathy in counseling and psychotherapy: Perspectives and practices. Routledge.

    Book  Google Scholar 

  • Clark, S. W., Gulin, S. L., Heller, M. B., & Vrana, S. R. (2017). Graduate training implications of the Q-interactive platform for administering Wechsler intelligence tests. Training and Education in Professional Psychology, 11(3), 148–155. https://doi.org/10.1037/tep0000155

    Article  Google Scholar 

  • Clements, L. M., & Kockelman, K. M. (2017). Economic effects of automated vehicles. Transportation Research Record, 2606(1), 106–114. https://doi.org/10.3141/2606-14

    Article  Google Scholar 

  • Daniel, M. H., Wahstrom, D., & Zang, O. (2014). Q–interactive equivalence of Q-interactive® and paper administrations of cognitive tasks: WISC®–V. (Q-Interactive Technical Report 8). Pearson.

    Google Scholar 

  • Darcy, A. M., Louie, A. K., & Roberts, L. W. (2016). Machine learning and the profession of medicine. Journal of American Medical Association, 315(6), 551–552. https://doi.org/10.1001/jama.2015.18421

    Article  Google Scholar 

  • Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation). MIT Sloan School of Management.

    Google Scholar 

  • Ding, X., Yue, X., Zheng, R., Bi, C., Li, D., & Yao, G. (2019). Classifying major depression patients and healthy controls using EEG, eye tracking and galvanic skin response data. Journal of Affective Disorders, 251, 156–161. https://doi.org/10.1016/j.jad.2019.03.058

    Article  PubMed  Google Scholar 

  • DiPaola, S., Gabora, L., & McCaig, G. (2018). Informing artificial intelligence generative techniques using cognitive theories of human creativity. Procedia Computer Science, 145, 158–168. https://doi.org/10.1016/j.procs.2018.11.024

    Article  Google Scholar 

  • Downing, S. M., & Haladyna, T. M. (2009). Validity and its threats. In S. M. Downing & R. Yudkowsky (Eds.), Assessment in health professions education (pp. 21–56). Routledge.

    Chapter  Google Scholar 

  • Dykes, P. C., & Donahue, M. (2018). Clinical judgment. In J. Fitzpatrick (Ed.), Encyclopedia of nursing research (4th ed., pp. 92–96). Springer Publishing Company. https://doi.org/10.1016/j.procs.2018.11.024

    Chapter  Google Scholar 

  • Dzedzickis, A., Kaklauskas, A., & Bučinskas, V. (2020). Human emotion recognition: Review of sensors and methods. Sensors, 20. https://doi.org/10.3390/s20030592

  • Elliott, R., Bohart, A. C., Watson, J. C., & Murphy, D. (2018). Therapist empathy and client outcome: An updated meta-analysis. Psychotherapy, 55, 399–410. https://doi.org/10.1037/pst0000175

    Article  PubMed  Google Scholar 

  • Elliott, S. N., & Gresham, F. M. (2008a). Social Skills Improvement System classwide intervention program. Pearson.

    Google Scholar 

  • Elliott, S. N., & Gresham, F. M. (2008b). Social Skills Improvement System intervention guide. Pearson.

    Google Scholar 

  • Elliott, S. N., & Gresham, F. M. (2008c). SSIS Performance screening guide. Pearson.

    Google Scholar 

  • Elliott, S. N., & Gresham, F. M. (2008d). Social Skills Improvement System rating scales. Pearson.

    Google Scholar 

  • Erchul, W. P., & Ward, C. S. (2016). Problem-solving consultation. In S. Jimerson, M. K. Burns, & A. M. VanDerHeyden (Eds.), Handbook of response to intervention (pp. 73–86). Springer.

    Chapter  Google Scholar 

  • Evans, L. D. (1991). Standard score comparison (Version 2.0) [Software program]. WtL Publishing.

    Google Scholar 

  • FastBridge Learning. (2021). FastBridge's story - Transforming assessments for educators. https://www.fastbridge.org/our-story/

  • Ferguson, C. J., & Heene, M. (2012). A vast graveyard of undead theories: Publication bias and psychological science’s aversion to the null. Perspectives on Psychological Science, 7(6), 555–561. https://doi.org/10.1177/1745691612459059

    Article  PubMed  Google Scholar 

  • Fischer, A. J., Dart, E. H., Radley, K. C., Richardson, D., Clark, R., & Wimberly, J. (2017). An evaluation of the effectiveness and acceptability of teleconsultation. Journal of Educational and Psychological Consultation, 27(4), 437–458. https://doi.org/10.1080/10474412.2016.1235978

    Article  Google Scholar 

  • Fisher, A. (2011). Critical thinking: An introduction. Cambridge University Press.

    Google Scholar 

  • Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. Journal of Medical Internet Research Mental. Health, 4(2) online. https://doi.org/10.2196/mental.7785

  • Flanagan, D. P., Ortiz, S. O., & Alfonso, V. C. (2013). Essentials of cross-battery assessment (Vol. 84). John Wiley & Sons.

    Google Scholar 

  • Flanagan, D. P., Ortiz, S. O., & Alfonso, V. C. (2017). XBASS: Cross-battery assessment software system (Version 2.0). Wiley.

    Google Scholar 

  • Flanagan, D. P., & Schneider, W. J. (2016). Cross-Battery Assessment? XBA PSW? A case of mistaken identity: A commentary on Kranzler and colleagues Classification agreement analysis of Cross-Battery Assessment in the identification of specific learning disorders in children and youth. International Journal of School & Educational Psychology, 4(3), 137–145. https://doi.org/10.1080/21683603.2016.1192852

    Article  Google Scholar 

  • Fleuren, L. M., Klausch, T. L., Zwager, C. L., Schoonmade, L. J., Guo, T., Roggeveen, L. F., Swart, E. L., Girbes, A. R. J., Thoral, P., Ercole, A., & Elbers, P. W. (2020). Machine learning for the prediction of sepsis: A systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Medicine, 46, 383–400. https://doi.org/10.1007/s00134-019-05872-y

    Article  PubMed  PubMed Central  Google Scholar 

  • Ford, M. (2015). Rise of the robots: Technology and the threat of a jobless future. Basic Books.

    Google Scholar 

  • Fulmer, R. (2019). Artificial intelligence and counseling: Four levels of implementation. Theory & Psychology, 29(6), 807–819. https://doi.org/10.1177/0959354319853045

    Article  Google Scholar 

  • Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018). Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: Randomized controlled trial. Journal of Medical Internet Research Mental Health, 5(4) online. https://doi.org/10.2196/mental.9782

  • Gaggioli, A. (2017). Artificial intelligence: The future of cybertherapy? Cyberpsychology, Behavior, and Social Networking, 20(6), 402–403. https://doi.org/10.1089/cyber.2017.29075.csi

    Article  Google Scholar 

  • Garg, P., & Glick, S. (2018). AI’s potential to diagnose and treat mental illness. Harvard Business Review, 22, 8–13.

    Google Scholar 

  • Gasparetto, A., & Scalera, L. (2019). From the Unimate to the Delta robot: The early decades of industrial robotics. In B. Zhang & M. Ceccarelli (Eds.), Explorations in the history and heritage of machines and mechanisms (pp. 284–295). Springer.

    Google Scholar 

  • Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864

    Article  Google Scholar 

  • Gresham, F. M., Elliott, S. N. (1990). Social Skills Rating System manual. AGS

  • Gresham, F. M., Elliott, S. N. (2008). Social Skills Improvement System performance screening guide manual. Pearson

  • Gutkin, T. B., & Curtis, M. J. (2009). School-based consultation: The science and practice of indirect service delivery. In T. B. Gutkin & C. R. Reynolds (Eds.), Handbook of school psychology (pp. 591–635). John Wiley & Sons.

    Google Scholar 

  • Institute of Education Sciences (IES. (2018). Digest of education statistics: Percentage of public-school students enrolled in gifted and talented programs, by sex, race / ethnicity, and state: Selected years, 2004 through 2013-14. National Center for Education Statistics https://nces.ed.gov/programs/digest/d18/tables/dt18_204.90.asp

    Google Scholar 

  • Institute of Education Sciences (IES, 2020). The condition of education: Students with disabilities. National Center for Education Statistics. https://nces.ed.gov/programs/coe/indicator_cgg.asp

    Google Scholar 

  • Jordan, M. I. (2019). Artificial intelligence - the revolution hasn’t happened yet. Harvard Data Science Review, 1(1) online. https://doi.org/10.1162/99608f92.f06c6e61

  • Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212. https://doi.org/10.1016/j.techsoc.2019.101212

    Article  Google Scholar 

  • Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37–50. https://doi.org/10.1016/j.bushor.2019.09.003

    Article  Google Scholar 

  • Krach, S. K., Kern, L. R., & McCreery, M. P. (2019). You can lead the teacher to the computer-based intervention …. Poster presentation at Annual Convention.

    Google Scholar 

  • Krach, S. K., & McCreery, M. P. (2016). Technology and positive behavioral interventions and support: Evaluation, selection, and implementation of computer-based socioemotional training. In S. Y. Tettegah & D. L. Espelage (Eds.), Emotions, Technology, and Behaviors (pp. 159–177). Academic Press. https://doi.org/10.1016/B978-0-12-801873-6.00009-1

    Chapter  Google Scholar 

  • Krach, S. K., McCreery, M. P., Dennis, L., Guerard, J., & Harris, E. L. (2020). Independent evaluation of Q-Interactive: A paper equivalency comparison using the PPVT-4 with preschoolers. Psychology in the Schools, 57(1), 17–30. https://doi.org/10.1002/pits.22325

    Article  Google Scholar 

  • Krach, S. K., McCreery, M. P., Doss, K. M., & Highsmith, D. M. (2020). Can computers teach social skills to children? Examining the efficacy of "The Social Express" with an African American sample. Contemporary School Psychology, 25(3), 321–331. https://doi.org/10.1007/s40688-019-00270-z

    Article  Google Scholar 

  • Krach, S. K., McCreery, M. P., & Rimel, H. (2017). Examining teachers' behavioral management charts: A comparison of Class Dojo and paper-pencil methods. Contemporary School Psychology, 21(3), 267–275. https://doi.org/10.1007/s40688-016-0111-0

    Article  Google Scholar 

  • Krach, S. K., Paskiewicz, T. L., Ballard, S. C., Howell, J. E., & Botana, S. M. (2021). Meeting the COVID-19 deadlines: Choosing assessments to determine eligibility. Journal of Psychoeducational Assessment, 39(1), 50–73. https://doi.org/10.1177/0734282920969993

    Article  PubMed  Google Scholar 

  • Kranzler, J. H., Floyd, R. G., Benson, N., Zaboski, B., & Thibodaux, L. (2016a). Classification agreement analysis of Cross-Battery Assessment in the identification of specific learning disorders in children and youth. International Journal of School & Educational Psychology, 4(3), 124–136. https://doi.org/10.1080/21683603.2016.1155515

    Article  Google Scholar 

  • Kranzler, J. H., Floyd, R. G., Benson, N., Zaboski, B., & Thibodaux, L. (2016b). Cross-Battery Assessment pattern of strengths and weaknesses approach to the identification of specific learning disorders: Evidence-based practice or pseudoscience? International Journal of School & Educational Psychology, 4(3), 146–157. https://doi.org/10.1080/21683603.2016.1192855

    Article  Google Scholar 

  • Kranzler, J. H., Maki, K. E., Benson, N. F., Eckert, T. L., Floyd, R. G., & Fefer, S. A. (2020). How do school psychologists interpret intelligence tests for the identification of specific learning disability? Contemporary School Psychology, 24, 445–456. https://doi.org/10.1007/s40688-020-00274-0

    Article  Google Scholar 

  • Krittanawong, C., Virk, H. U. H., Bangalore, S., Wang, Z., Johnson, K. W., Pinotti, R., Zhang, J. J., Kaplin, S., Narasimhan, B., Kitai, T., Baber, U., Halperin, J. L., & Tang, W. W. (2020). Machine learning prediction in cardiovascular diseases: A meta-analysis. Nature: Scientific Reports, 10(1) online. https://doi.org/10.1038/s41598-020-72685-1

  • Kühberger, A., Fritz, A., & Scherndl, T. (2014). Publication bias in psychology: A diagnosis based on the correlation between effect size and sample size. PloS one, 9(9), e105825. https://doi.org/10.1371/journal.pone.0105825

    Article  PubMed  PubMed Central  Google Scholar 

  • Larson, M., Cook, C. R., Fiat, A., & Lyon, A. R. (2018). Stressed teachers don’t make good implementers: Examining the interplay between stress reduction and intervention fidelity. School Mental Health, 10(1), 61–76. https://doi.org/10.1007/s12310-018-9250-y

    Article  Google Scholar 

  • Law, G. C., Dutt, A., & Neihart, M. (2019). Increasing intervention fidelity among special education teachers for autism intervention: A pilot study of utilizing a mobile-app-enabled training program. Research in Autism Spectrum Disorders, 67, 1–11. https://doi.org/10.1016/j.rasd.2019.101411

    Article  Google Scholar 

  • Lee, Y., Ragguett, R. M., Mansur, R. B., Boutilier, J. J., Rosenblat, J. D., Trevizol, A., Brietzke, E., Lin, K., Pan, Z., Subramaniapillai, M., Chan, T. C. Y., Fus, D., Park, C., Musial, N., Zuckerman, H., Chen, V. C. H., Ho, R., Rong, C., & McIntyre, R. S. (2018). Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. Journal of Affective Disorders, 241, 519–532. https://doi.org/10.1016/j.jad.2018.08.073

    Article  PubMed  Google Scholar 

  • Li, Q. (2007). Student and teacher views about technology: A tale of two cities? Journal of Research on Technology in Education, 39(4), 377–397. https://doi.org/10.1080/15391523.2007.10782488

    Article  Google Scholar 

  • Lichtenberger, E. O. (2006). Computer utilization and clinical judgment in psychological assessment reports. Journal of Clinical Psychology, 62(1), 19–32. https://doi.org/10.1002/jclp.20197

    Article  PubMed  Google Scholar 

  • Lisk, S., Vaswani, A., Linetzky, M., Bar-Haim, Y., & Lau, J. Y. (2020). Systematic review and meta-analysis: Eye-tracking of attention to threat in child and adolescent anxiety. Journal of the American Academy of Child & Adolescent Psychiatry, 59(1), 88–99. https://doi.org/10.1016/j.jaac.2019.06.006

    Article  Google Scholar 

  • Lockwood, A. B., Sealander, K., Gross, T. J., & Lanterman, C. (2020). Teacher trainees’ administration and scoring errors on the Kaufman Test of Educational Achievement. Journal of Psychoeducational Assessment, 38(5), 551–563. https://doi.org/10.1177/0734282919871144

    Article  Google Scholar 

  • McCreery, M. P., Krach, S. K., Bacos, C., & Laferriere, J. (2019). Can video games be used as a stealth assessment of aggression? A criterion-related validity study. International Journal of Gaming and Computer-Mediated Simulations, 11, 40–49. https://doi.org/10.4018/IJGCMS.20190401

    Article  Google Scholar 

  • McCreery, M. P., Schrader, P. G., Krach, S. K., & Boone, R. (2013). A sense of self: The role of presence in virtual environments. Computers in Human Behavior, 29(4), 1635–1640. https://doi.org/10.1016/j.chb.2011.12.019

    Article  Google Scholar 

  • McGill, R. J., Dombrowski, S. C., & Canivez, G. L. (2018). Cognitive profile analysis in school psychology: History, issues, and continued concerns. Journal of School Psychology, 71, 108–121. https://doi.org/10.1016/j.jsp.2018.10.007

    Article  PubMed  Google Scholar 

  • McKenna, J. W., & Parenti, M. (2017). Fidelity assessment to improve teacher instruction and school decision making. Journal of Applied School Psychology, 33(4), 331–346. https://doi.org/10.1080/15377903.2017.1316334

    Article  Google Scholar 

  • McNamara, K. M., Walcott, C. M., & Hyson, D. (2019). Results from the NASP 2015 membership survey. In Part two: Professional practices in school psychology [Research report]. National. Association of School Psychologists.

    Google Scholar 

  • Meehl, P. E. (1954). Clinical versus statistical prediction: A theoretical analysis and review of the evidence. University of Minnesota Press.

    Book  Google Scholar 

  • Mele, M. L., & Federici, S. (2012). Gaze and eye-tracking solutions for psychological research. Cognitive Processing, 13(1), 261–265. https://doi.org/10.1007/s10339-012-0499-z

    Article  Google Scholar 

  • Merten, E. C., Cwik, J. C., Margraf, J., & Schneider, S. (2017). Overdiagnosis of mental disorders in children and adolescents (in developed countries). Child and Adolescent Psychiatry and Mental Health, 11(5), online https://doi.org/10.1186/s13034-016-0140-5

  • Milchram, C., Van de Kaa, G., Doorn, N., & Künneke, R. (2018). Moral values as factors for social acceptance of smart grid technologies. Sustainability, 10(8), 2703. https://doi.org/10.3390/su10082703

    Article  Google Scholar 

  • Moore, A. L., & Miller, T. (2016). Gibson test of cognitive skills – version 2: Digital and interactive test. Technical manual for centers https://www.thegibsontest.com/

    Google Scholar 

  • Morley, J., Machado, C. C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in healthcare: A mapping review. Social Science & Medicine, 260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172

    Article  Google Scholar 

  • Nappi, C., & Cuocolo, A. (2020). The machine learning approach: Artificial intelligence is coming to support critical clinical thinking. Journal of Nuclear Cardiology, 27, 156–158. https://doi.org/10.1007/s12350-018-1344-2

    Article  PubMed  Google Scholar 

  • National Association of School Psychologists (NASP, 2020a). Graduate preparation of school psychologists. In The professional standards of the National Association of School Psychologists.

    Google Scholar 

  • National Association of School Psychologists (NASP, 2020b). Principles for ethical practice. In The professional standards of the National Association of School Psychologists.

    Google Scholar 

  • National Association of School Psychologists (NASP, 2020c). Model for comprehensive and integrated school psychological services in the professional standards of the national association of school psychologists.

  • Nietfeld, J., & Bosma, A. (2003). Examining the self-regulation of impulsive and reflective response styles on academic tasks. Journal of Research in Personality, 37(3), 118–140. https://doi.org/10.1016/S0092-6566(02)00564-0

    Article  Google Scholar 

  • Noland, R. M. (2017). Intelligence testing using a tablet computer: Experiences with using Q-interactive. Training and Education in Professional Psychology, 11(3), 156–163. https://doi.org/10.1037/tep0000149

    Article  Google Scholar 

  • Northwest Evaluation Association (2021). Measures of Academic Progress [Webpage] https://teach.mapnwea.org

    Google Scholar 

  • Noyes, J. M., & Garland, K. J. (2008). Computer-vs. paper-based tasks: Are they equivalent? Ergonomics, 51(9), 1352–1375. https://doi.org/10.1080/00140130802170387

    Article  PubMed  Google Scholar 

  • Ohme, R., Reykowska, D., Wiener, D., & Choromanska, A. (2009). Analysis of neurophysiological reactions to advertising stimuli by means of EEG and galvanic skin response measures. Journal of Neuroscience, Psychology, and Economics, 2(1), 21–31. https://doi.org/10.1037/a0015462

    Article  Google Scholar 

  • Özdemir, K. (2020). Analyzing the influence of culture on technology acceptance model (Master's thesis). http://hdl.handle.net/20.500.12416/4889

    Google Scholar 

  • Pearson. (2015). Aimsweb administration and technical manual. Pearson.

    Google Scholar 

  • Proscia, M., Ulrich, F., Nicolino, P., & Morote, E. S. (2010). Teachers’ attitude toward use of computers in the classroom and differentiated instruction and instructional technology. In D. Gibson & B. Dodge (Eds.), Proceedings of SITE 2010 - Society for Information Technology & Teacher Education international conference (pp. 3340–3347) Association for the Advancement of Computing in Education (AACE).

    Google Scholar 

  • Ranganathan, P., Pramesh, C. S., & Buyse, M. (2016). Common pitfalls in statistical analysis: The perils of multiple testing. Perspectives in Clinical Research, 7(2), 106–107. https://doi.org/10.4103/2229-3485.179436

    Article  PubMed  PubMed Central  Google Scholar 

  • Renaissance Learning (2014). The research foundation for STAR assessments.

    Google Scholar 

  • Reynolds, C. R., Kamphaus, R. (2015). Behavior Assessment System for Children, Third Edition (BASC-3) QGlobal comprehensive kit. Pearson

  • Reynolds, C. R., Stowe, M. L. (1985). Severe discrepancy analysis [Software Program]. Train

    Google Scholar 

  • Richman, W. L., Kiesler, S., Weisband, S., & Drasgow, F. (1999). A meta-analytic study of social desirability distortion in computer-administered questionnaires, traditional questionnaires, and interviews. Journal of Applied Psychology, 84(5), 754–775. https://doi.org/10.1037/0021-9010.84.5.754

    Article  Google Scholar 

  • Riley-Tillman, T. C., Burns, M. K., & Gibbons, K. (2013). RTI applications, Volume 2: Assessment, analysis, and decision making (Vol. 2). Guilford Press.

    Google Scholar 

  • Rogers, C. R. (1980). A way of being. Houghton Mifflin.

    Google Scholar 

  • Rogers, P. C., Graham, C. R., & Mayes, C. T. (2007). Cultural competence and instructional design: Exploration research into the delivery of online instruction cross-culturally. Educational Technology Research and Development, 55(2), 197–217. https://doi.org/10.1007/s11423-007-9033-x

    Article  Google Scholar 

  • Rosenfield, S. A., Gravois, T. A., & Silva, A. E. (2014). Bringing instructional consultation to scale. In W. P. Erchul & S. M. Sheridan (Eds.), Handbook of research in school consultation (pp. 248–275). Routledge.

    Google Scholar 

  • Ross, S. G., & Begeny, J. C. (2014). Single-case effect size calculation: Comparing regression and non-parametric approaches across previously published reading intervention data sets. Journal of School Psychology, 52(4), 419–431. https://doi.org/10.1016/j.jsp.2014.06.003

    Article  PubMed  Google Scholar 

  • Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96. https://doi.org/10.1080/10400419.2012.650092

    Article  Google Scholar 

  • Sattler, J. M. (2018). Assessment of children: Cognitive applications (6th ed.).

    Google Scholar 

  • Schmitt, A. J., Kolbert, J. B., Hughes, T. L., & Crothers, L. M. (2020). Stages, processes, and procedures in school-based consultation. In L. M. In, T. L. Crothers, J. B. Hughes, & Kolbert, & A. J. Schmitt. (Eds.), Theory and cases in school-based consultation (2nd ed., pp. 23–34). Routledge.

    Google Scholar 

  • Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge University Press.

    Book  Google Scholar 

  • Shute, V., & Ventura, M. (2013). Stealth assessment: Measuring and supporting learning in video games (p. 102). The MIT Press.

    Book  Google Scholar 

  • Smith, A., & Anderson, J. (2014). AI, robotics, and the future of jobs. Pew Research Center, 6, 51–129. https://www.pewresearch.org/internet/2014/08/06/future-of-jobs/

    Google Scholar 

  • Snyder, D. K. (2000). Computer-assisted judgment: Defining strengths and liabilities. Psychological Assessment, 12(1), 52–60. https://doi.org/10.1037/1040-3590.12.1.52

    Article  PubMed  Google Scholar 

  • Stormont, M., & Reinke, W. M. (2014). Providing performance feedback for teachers to increase treatment fidelity. Intervention in School and Clinic, 49(4), 219–224. https://doi.org/10.1177/1053451213509487

    Article  Google Scholar 

  • Sugai, G., & Horner, R. R. (2006). A promising approach for expanding and sustaining school-wide positive behavior support. School Psychology Review, 35(2), 245–259. https://doi.org/10.1080/02796015.2006.12087989

    Article  Google Scholar 

  • Tanner, C. A., Padrick, K. P., Westfall, U. E., & Putzier, D. J. (1987). Diagnostic reasoning strategies of nurses and nursing students. Nursing Research, 36(6), 358–363. https://doi.org/10.1097/00006199-198711000-00010

    Article  PubMed  Google Scholar 

  • Taylor, W. P., Miciak, J., Fletcher, J. M., & Francis, D. J. (2017). Cognitive discrepancy models for specific learning disabilities identification: Simulations of psychometric limitations. Psychological Assessment, 29(4), 446–457. https://doi.org/10.1037/pas0000356

    Article  PubMed  Google Scholar 

  • Tyler-Wood, T. L., Cockerham, D., & Johnson, K. R. (2018). Implementing new technologies in a middle school curriculum: A rural perspective. Smart Learning Environments, 5(1), 1–16. https://doi.org/10.1186/s40561-018-0073-y

    Article  Google Scholar 

  • Usman, H., Mulia, D., Chairy, C., & Widowati, N. (2020). Integrating trust, religiosity, and image into technology acceptance model: The case of the Islamic philanthropy in Indonesia. Journal of Islamic Marketing, 13(2), 381–409. https://doi.org/10.1108/JIMA-01-2020-0020

    Article  Google Scholar 

  • Van Braak, J. (2001). Individual characteristics influencing teachers' class use of computers. Journal of Educational Computing Research, 25(2), 141–157. https://doi.org/10.2190/81YV-CGMU-5HPM-04EG

    Article  Google Scholar 

  • Vargas-Cuentas, N. I., Roman-Gonzalez, A., Gilman, R. H., Barrientos, F., Ting, J., Hidalgo, D., Jensen, K., & Zimic, M. (2017). Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children. PloS one, 12(11), e0188826. https://doi.org/10.1371/journal.pone.0188826

    Article  PubMed  PubMed Central  Google Scholar 

  • Vormittag, I., & Ortner, T. M. (2014). In the eye of the examinee: Likable examiners interfere with performance. Social Psychology of Education, 17(3), 401–417. https://doi.org/10.1007/s11218-014-9252-z

    Article  Google Scholar 

  • Weijters, B., Geuens, M., & Schillewaert, N. (2010). The stability of individual response styles. Psychological Methods, 15(1), 96–110. https://doi.org/10.1037/a0018721

    Article  PubMed  Google Scholar 

  • Yalcin, ӦN., & DiPaola, S. (2018). A computational model of empathy for interactive agents. Biologically Inspired Cognitive Architectures, 26, 20–25. https://doi.org/10.1016/j.bica.2018.07.010

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by S. Kathleen Krach and Stephanie Corcoran. The first draft of the manuscript was written by S. Kathleen Krach and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Krach, S.K., Corcoran, S. Will Computers Replace School Psychologists? An Analysis of Tech-Based Tools for Assessment, Consultation, and Counseling. Contemp School Psychol (2023). https://doi.org/10.1007/s40688-023-00455-7

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