For decades, multiple-choice questions (MCQs) have formed the backbone of assessment practices in education and certification. Their popularity stems from their efficiency, scalability, and ease of scoring, especially in large-scale, high-stakes environments.
However, as digital transformation accelerates and educational goals evolve, questions are being raised about the efficacy and fairness of MCQs in evaluating deeper cognitive abilities. The rise of artificial intelligence (AI), particularly in automated scoring, is enabling a shift toward open-ended assessment formats previously considered too resource-intensive to scale.
This article explores the current academic discourse surrounding MCQs, the potential of AI-assisted open-ended assessments, and how TestInvite is positioned to drive this transformation.
MCQs offer unmatched logistical advantages: they are quick to administer, easy to grade, and allow objective scoring with minimal bias (7). Their ability to cover broad content areas in a single test makes them especially valuable in standardized testing.
Despite their strengths, research indicates that many MCQ-based tests suffer from psychometric weaknesses. A study analyzing MCQ tests from various academic programs reported an average reliability coefficient of 0.54, with several tests falling below acceptable reliability thresholds (8). Moreover, poorly constructed distractors diminish the discriminatory power of items, weakening the overall validity of the assessment.
In a study by DiBattista and Kurzawa (5), MCQs with distractors derived from actual student errors demonstrated significantly better psychometric performance compared to traditional distractor construction. These findings suggest that even minor design improvements can have major effects on test quality, though many institutions lack the capacity for such optimization.
MCQs often focus on factual recall and recognition rather than critical thinking, synthesis, or application (3). While it is theoretically possible to construct higher-order MCQs aligned with Bloom's taxonomy, in practice most items target lower cognitive levels due to time constraints and item-writing complexity (2).
Open-ended questions like essays, short answers, reflections are widely regarded as more valid tools for assessing higher-order thinking, problem-solving, and communication skills (1). Yet, their adoption in large-scale testing has been hindered by logistical challenges: time-consuming grading, inter-rater variability, and increased administrative burden.
Automated essay scoring systems, initially rule-based and now increasingly AI-driven, promise to overcome these limitations. Research shows that AES systems can approximate human scoring reasonably well, especially when calibrated against detailed rubrics and large datasets (9).
Recent developments in large language models (LLMs) like GPT-4 have expanded AES capabilities, enabling scoring of not just grammar and coherence but also argumentation, creativity, and contextual relevance. However, these models also raise new concerns about transparency, fairness, and alignment with human judgment (4).
The reliability of AI scoring varies significantly by task type and domain. In a study of generative AI scoring on student essays, agreement with human raters was strong in areas like clarity and coherence but weak in assessing task relevance and originality (10). Hybrid scoring models, combining AI and human ratings, show promise in maximizing reliability and reducing biases (11).
To ensure valid and equitable use, best practices recommend rigorous validation, transparent rubrics, bias audits, and continuous calibration of AI models (6).
TestInvite is well-equipped to facilitate this paradigm shift by integrating AI-assisted open-ended assessments alongside traditional formats.
By embedding these capabilities into its platform, TestInvite empowers institutions to modernize assessments in alignment with evolving educational goals.
Despite the promise, transitioning to AI-scored open-ended assessments involves significant considerations:
The throne of MCQs may not be collapsing, but it is certainly being shared. Even though they are not disappearing, their long-standing monopoly is ending.
As education evolves to emphasize critical thinking, creativity, and real-world application, assessment systems must follow suit. Advances in AI scoring make it feasible to use open-ended formats at scale, challenging the long-standing dominance of MCQs.
TestInvite is an active enabler of this transformation, giving educators the tools to measure not just what learners know, but how they think. By combining the scalability of MCQs with the depth of constructed responses enhanced by AI, TestInvite offers a future of assessment that is both efficient and meaningful.
(1) Bennett, R. E., Braswell, J., Oranje, A., Sandene, B. A., Kaplan, B. A., & Yan, F. (2010). Does it matter if I take my mathematics test on computer? A second empirical study of mode effects in NAEP. Journal of Technology, Learning, and Assessment, 8(6), 1-38.
(2) Brame, C. J. (2013). Writing good multiple choice test questions. Center for Teaching, Vanderbilt University.
(3) Brookhart, S. M. (2010). How to assess higher-order thinking skills in your classroom. ASCD.
(4) Clark, A., Gomez, R., & Tannenbaum, R. (2025). Human-AI scoring comparisons on open-ended writing tasks. Educational Measurement: Issues and Practice, 44(2), 21–35.
(5) DiBattista, D., & Kurzawa, L. (2011). Examination of the quality of multiple-choice items on classroom tests. The Canadian Journal for the Scholarship of Teaching and Learning, 2(2), Article 4.
(6) ETS. (2024). Best practices for AI scoring in large-scale assessments. Educational Testing Service.
(7) Haladyna, T. M., & Rodriguez, M. C. (2013). Developing and validating test items. Routledge.
(8) Oladipo, S. E., & Idowu, O. O. (2013). Psychometric analysis of multiple choice test items of University Examination. International Journal of Management and Social Sciences Research, 2(1), 6–10.
(9) Shermis, M. D., & Burstein, J. (Eds.). (2013). Handbook of automated essay evaluation: Current applications and new directions. Routledge.
(10) Sun, Y., Lin, H., & Zhao, T. (2025). Evaluating LLM scoring reliability across disciplines. Journal of Educational Assessment and Analytics, 12(1), 33–47.
(11) Wei, X., Mahmood, R., & Kapoor, S. (2025). Hybrid scoring models: Combining human and AI assessments. Computers & Education, 212, 104878.
