Gagan Shergill

Multilingual (ML) students represent a growing portion of the U.S. public school population, yet school psychologists often lack the linguistic resources needed to provide translated psychoeducational materials. Machine translation (MT) tools such as Google Translate (GT) and ChatGPT-4o (GPT) offer potential solutions, but their effectiveness for translating psychoeducational reports remains underexamined. This study evaluated the fluency, accuracy, and error patterns of English-to-Spanish translations generated by GT, GPT, and a professional human translator (HT) using a fictional psychoeducational report summary. Two bilingual graduate students rated 71 translated sentences using fluency and accuracy rubrics, with disagreements resolved by a bilingual school psychologist. Results indicated no significant differences in fluency across systems. Accuracy differed significantly across systems overall, although follow-up pairwise comparisons were not statistically significant after correction. Analyses also revealed significant differences in the total number of errors produced across translations, but no significant differences in the distribution of specific error types. Across systems, errors included mistranslations, grammatical errors, untranslated words, and critical inaccuracies that affected interpretability. Overall, no translation method produced a perfect translation. However, results remain preliminary and require further study. Implications for school psychologists include the need to improve the readability of psychoeducational reports and ensure that translators’ workflows are supported by appropriate training and oversight.

Objective:Artificial intelligence (AI) is increasingly used across healthcare and education professions, yet cross-professional evidence on adoption and governance practices remains limited. This study examined differences across allied health and related service professionals in AI use, disclosure practices, beliefs about informed consent, and training experiences.Methods:Participants were 1,350 allied health and related service professionals practicing in Ohio, including physical therapists, occupational therapists, speech-language pathologists, school psychologists, counselors, nurses, and assistant-level providers. The survey was adapted from prior school psychology AI studies and refined with multidisciplinary task force input. Analyses examined differences across professional roles in frequency of AI use, disclosure practices, informed consent beliefs, and type of AI training received. Missing data were addressed using multiple imputation.Results:AI use differed significantly across professional roles, with school psychologists, counselors, and speech-language pathologists reporting the highest levels of use, and occupational therapy assistants and physical therapy assistants reporting the lowest. Disclosure practices also varied significantly, with school psychologists and speech-language pathologists reporting greater disclosure than several other groups. Differences in informed consent practices were statistically significant but small, with school psychologists, counselors, and speech-language pathologists somewhat more likely to report obtaining consent, and assistants and nurses somewhat more likely to report not obtaining consent. Training disparities were pronounced: school psychologists, counselors, and speech-language pathologists were more likely to report formal AI training, whereas assistants were substantially more likely to report no AI training.Conclusions:AI adoption and governance-related practices vary meaningfully across school-based and related service professions. These differences suggest uneven preparedness for ethical and coordinated AI integration across professional roles. Findings underscore the need for clearer guidance, stronger training pathways, and greater attention to disclosure and consent practices as AI becomes more embedded in professional service delivery.

The rapid adoption of artificial intelligence in school psychology has created an urgent need for ethical guidance, as practitioners increasingly use AI tools without clear professional standards or institutional policies. This paper applies existing American Psychological Association and National Association of School Psychologists ethical principles to AI-specific challenges in school psychology practice. We examine how fundamental ethical obligations (i.e., beneficence and nonmaleficence, fidelity and responsibility, integrity, justice, and respect for rights and dignity) apply to concerns including algorithmic bias, privacy breaches, professional deskilling, and output inaccuracies. The paper provides comprehensive implementation recommendations for practitioners, trainers and organizations. By grounding AI use in established ethical frameworks, this guidance enables school psychologists to leverage AI benefits while maintaining professional standards, ensuring accurate services, and protecting student welfare in an evolving technological landscape.

Correspondence concerning this article should be addressed to Adam Lockwood, School of Lifespan Development and Educational Sciences, Kent State University, 150 Terrace Drive, Kent, OH 44242, USA; adamblockwood@gmail.com.

This study examines the effectiveness of artificial intelligence (AI) in psychological report writing by comparing reports generated by human psychologists with those produced by OpenAI’s Generative Pre-trained Transformer Version 4 (ChatGPT-4). A total of 249 licensed psychologists evaluated the reports based on overall quality, readability, writing style, organization, summary quality, recommendations, preference, and willingness to sign off on the reports. Although human-generated reports were generally rated more favorably and participants expressed greater comfort in approving them, effect sizes were typically small. Two exceptions were noted: moderate effect sizes were found in favor of human-written summaries, while AI-generated reports showed moderate effect sizes for the quality of their recommendations. These findings suggest that AI shows potential for augmenting report writing. Comprehensive guidelines are necessary for the ethical and effective integration of AI into psychological practice. Further research is needed to enhance our understanding of AI’s role and capabilities in psychological assessment and reporting.

The Comprehensive Test of Phonological Processing, 2^nd Edition (CTOPP-2; Wagner et al., 2013) is commonly used in k-12 public schools to assess basic cognitive processing skills foundational for reading achievement. Psychometric support for its use with dual language learners (DLLs), a group representing over 10% of the school-aged population in the United States, is critical. This study tested the metric and scaler invariance of CTOPP-2 scores among school-aged children ( n = 242; 41.3% Spanish-speaking DLL). Results indicate that the CTOPP-2’s three-factor (i.e., Phonological Awareness, Phonological Memory, and Rapid Automatic Naming) measurement structure displays metric and scalar invariance for DLLs. Model fit was improved when the Phonological Awareness and Phonological Memory factors were combined. Implications for future research and the practice of psychoeducational diagnostic assessment with DLLs are discussed.