The Limits of AI Coding: When Language Models Miss the Mark

Author: Denis Avetisyan

New research reveals that while large language models show promise in coding qualitative data, they struggle with nuanced analysis and rare but critical themes.

The design facilitates a collaborative workflow between human expertise and artificial intelligence to address Research Question 2, establishing a framework where insights are iteratively refined through a coupled human-AI system.

A human-in-the-loop workflow combining transformer models and expert adjudication improves the reliability of deductive coding, particularly when dealing with imbalanced datasets and qualitative learning analytics.

Despite the promise of automated coding for scaling qualitative analysis in learning analytics, current generative AI models often struggle with nuanced, theory-driven interpretations. This study, ‘When LLMs fall short in Deductive Coding: Model Comparison and Human AI Collaboration Workflow Design’, comparatively assesses the performance of large language models and smaller transformer classifiers on deductive coding tasks, particularly with imbalanced data distributions. Our findings reveal that LLMs do not outperform established models and exhibit systematic errors, necessitating human oversight; however, a collaborative human-AI workflow significantly improves both coding efficiency and reliability. Can strategically designed human-in-the-loop systems effectively bridge the gap between automated efficiency and rigorous theoretical coding?

The Imperative of Pattern Extraction from Qualitative Data

Historically, extracting meaning from qualitative data – encompassing interviews, open-ended survey responses, and textual documents – has been a profoundly manual undertaking. Researchers meticulously pore over transcripts and field notes, identifying themes and patterns through a process susceptible to individual interpretation and inherent biases. This labor-intensive nature not only limits the scale of analysis but also introduces questions regarding the reliability and validity of the findings, as different researchers may arrive at divergent conclusions when examining the same material. The subjective element, while unavoidable to a degree, presents a significant challenge to establishing robust and defensible insights from rich, narrative data.

The proliferation of digital data sources – from social media feeds and online reviews to open-ended survey responses – has created an unprecedented volume of qualitative information. Manually analyzing this influx is no longer feasible, driving the development of automated techniques like natural language processing and machine learning. However, these approaches aren’t without challenges; algorithms can misinterpret nuance, context, and cultural subtleties inherent in human language. Consequently, careful validation and human oversight remain crucial to ensure the accuracy and reliability of insights derived from automated qualitative data analysis, preventing the propagation of biases and ensuring meaningful conclusions are reached.

Experiment 2 revealed that both models demonstrate a significant bias towards predicting negative outcomes.

Automated Coding: A Paradigm Shift in Qualitative Analysis

Auto-coding leverages Large Language Models (LLMs) to automate the process of qualitative data analysis, specifically the assignment of codes to textual data. Traditional qualitative analysis relies on manual coding, a time-consuming process where researchers identify themes and patterns within data and label segments accordingly. LLMs, trained on vast datasets, can now perform this task with increasing accuracy, identifying relevant segments and assigning pre-defined or emergent codes. This automation substantially reduces the time required for analysis, enabling researchers to process larger datasets and focus on interpretation rather than manual tagging. The speed gained through auto-coding facilitates more iterative research cycles and allows for quicker identification of key insights within qualitative data.

The Transformer architecture, central to the functionality of Large Language Models (LLMs) used in auto-coding, relies on self-attention mechanisms to weigh the importance of different words in a text sequence. Unlike recurrent neural networks that process data sequentially, Transformers process the entire input simultaneously, allowing for parallelization and faster training. This architecture consists of an encoder and a decoder; the encoder maps the input sequence into a high-dimensional vector representation, capturing contextual relationships, while the decoder generates the output sequence. Key to this process is the attention mechanism, which enables the model to focus on relevant parts of the input when processing each word, thus facilitating a nuanced understanding of complex textual information and improving performance on tasks like code assignment.

The performance of auto-coding systems is susceptible to issues arising from imbalanced datasets. When the frequency of codes within a qualitative dataset varies significantly – with some codes appearing far more often than others – LLMs can exhibit a bias toward the prevalent codes. This occurs because the model is trained on the distribution of the data, leading to higher precision for frequent codes but reduced ability to accurately identify instances of less common, yet potentially crucial, codes. Consequently, analyses relying on auto-coding with imbalanced data may underrepresent minority themes and skew overall findings, necessitating careful review and potential manual correction of results.

Addressing Imbalance: Strategies for Robust Analysis

Long-tail distributions frequently occur in qualitative datasets, wherein a small number of categories account for the majority of instances while a large number of categories each have very few instances. This imbalance presents a challenge for auto-coding applications, as algorithms may struggle to accurately predict or assign labels to the less frequent categories due to insufficient training examples. The disproportionate representation can lead to a bias towards the majority classes, diminishing the overall predictive power and increasing the likelihood of misclassification for instances belonging to minority classes. Consequently, models trained on these datasets may exhibit poor generalization performance and require specific mitigation strategies to address the inherent class imbalance.

Oversampling techniques address class imbalance by replicating instances from the minority class, directly increasing its representation in the training dataset. Synthetic Minority Oversampling Technique (SMOTE) generates new, synthetic instances of the minority class based on feature space similarities between existing minority class examples, rather than simply duplicating them. Cost-Sensitive Learning assigns higher misclassification costs to the minority class during model training; this compels the algorithm to prioritize correct classification of minority class instances, even at the expense of potentially increased errors on the majority class. These methods aim to provide a more balanced dataset, thereby improving the model’s ability to generalize and accurately predict instances from all classes.

While oversampling, SMOTE, and cost-sensitive learning address imbalanced datasets, rigorous validation is crucial to prevent unintended consequences. Oversampling can lead to the model memorizing minority class examples, resulting in poor generalization to unseen data. SMOTE, by creating synthetic samples, may introduce artificial patterns not present in the original distribution. Cost-sensitive learning, if improperly calibrated, can disproportionately penalize or reward specific classes, biasing the model’s predictions. Therefore, techniques like k-fold cross-validation, utilizing separate validation and test sets, and monitoring performance metrics beyond overall accuracy – such as precision, recall, and F1-score – are essential to assess the model’s true predictive capability and prevent overfitting to the imbalanced training data.

Synergy of Human Insight and Automated Precision

Automated coding techniques promise substantial efficiency in data analysis, yet relying solely on these methods risks compromising the validity and accuracy of resulting insights. While algorithms can rapidly process large datasets, they often struggle with nuanced or ambiguous cases requiring contextual understanding. Consequently, integrating human expertise into the coding workflow becomes crucial; this collaborative approach allows subject matter experts to review and refine the algorithm’s output, particularly focusing on instances where the AI exhibits low confidence or proposes less common codes. This synergy does not aim to replace automation, but rather to augment it, ensuring that the benefits of speed are matched by the reliability of the findings and enabling a more comprehensive and trustworthy analysis of complex data.

Automated coding systems, while efficient, benefit significantly from strategic human oversight. Techniques such as Selective Prediction – where the AI flags instances requiring review – and Confidence Thresholding, which prioritizes cases with lower certainty scores, enable human experts to concentrate efforts on the most challenging data. Furthermore, employing Deductive Coding – utilizing predefined codebooks to guide the AI – provides a structured framework for analysis and facilitates targeted refinement. This collaborative approach does not aim to replace automation, but rather to augment it, allowing human expertise to resolve ambiguities and ensure the accuracy of insights generated by the system, ultimately leading to more reliable and nuanced understandings of complex data.

Research indicates that combining automated coding with human expertise significantly enhances the reliability of qualitative data analysis. A recent study demonstrated an improvement in coding consistency, measured by Cohen’s Kappa, from 0.62 when utilizing the MacBERT model independently, to 0.66 with the integration of human review. This collaborative workflow proved particularly impactful when addressing less frequent codes; human adjudication of these rare instances resulted in a substantial 0.20 increase in Cohen’s Kappa specifically for those codes, highlighting the critical role of expert oversight in refining automated insights and ensuring a more nuanced and accurate understanding of complex data.

Towards a Future of Learning Analytics: Precision and Interpretation

The convergence of automated coding techniques and human analytical skills promises a transformative leap forward for learning analytics. While algorithms like MacBERT demonstrate impressive capacity for processing educational data, their true potential is realized when paired with expert interpretation. This synergy allows for both the scalability needed to analyze vast datasets and the nuanced understanding required to identify meaningful patterns in learner behavior. By automating initial coding stages, researchers can focus their efforts on validating findings, exploring emergent themes, and ultimately, developing more effective and personalized educational interventions. This integrated approach does not simply accelerate analysis; it enhances its depth, ensuring that data-driven insights translate into tangible improvements in learning outcomes and a more comprehensive understanding of the learning process itself.

The increasing volume and complexity of educational data demand innovative analytical approaches, and models like MacBERT offer a powerful solution when applied to deductive coding. These transformer-based language models, pre-trained on massive text corpora, excel at understanding semantic relationships and contextual nuances within student work – everything from essays and discussion posts to code submissions. By leveraging MacBERT, researchers and educators can automate the process of assigning pre-defined codes to qualitative data with significantly improved accuracy and consistency compared to manual coding. This automation does not simply expedite analysis; it allows for the processing of far larger datasets, revealing patterns and insights that would remain hidden through traditional methods. The scalability afforded by these models enables a more comprehensive understanding of student learning behaviors, facilitating data-driven improvements to instructional design and personalized learning experiences.

Beyond the precision of automated and deductive analyses, inductive coding offers a crucial pathway to uncovering nuanced understandings of the learning experience. This approach eschews pre-defined categories, instead allowing prominent themes and unexpected patterns to arise directly from the data itself – be it student writing, discussion forum posts, or interaction logs. By remaining open to emergent insights, researchers can identify previously unrecognized learner behaviors, motivations, and challenges. This iterative process of data exploration fosters a more holistic and ecologically valid comprehension of how learning unfolds, supplementing quantitative findings with rich, qualitative detail and ultimately informing more responsive and effective educational practices.

The pursuit of reliable deductive coding, as detailed in the research, echoes a fundamental tenet of mathematical rigor. Hilbert famously stated, “In every well-defined mathematical problem there is a method to solve it.” This sentiment directly applies to the challenge LLMs face with imbalanced data and rare codes; the method – a purely algorithmic approach – proves insufficient. The study demonstrates that while LLMs can attempt to deduce codes, the lack of provability, the inability to consistently reproduce results, necessitates human adjudication. This collaborative workflow isn’t merely about improving accuracy; it’s about safeguarding the theoretical insights embedded within qualitative analysis, ensuring the ‘solution’ is demonstrably correct, not just seemingly functional.

What’s Next?

The observed fallibility of Large Language Models in deductive coding, particularly concerning infrequent but theoretically significant codes, is not a surprising result, but a necessary clarification. The pursuit of ‘working’ solutions, measured by performance on arbitrarily constructed datasets, frequently obscures the fundamental requirement of correctness. A model that reliably identifies a common pattern while failing to detect a critical exception, however rare, offers little genuine advancement. The focus must shift from empirical performance to provable guarantees, even if such guarantees initially necessitate drastically simplified problem spaces.

Future research should prioritize the development of formal methods for validating coding schemes produced by these models. The current human-in-the-loop approach, while pragmatic, merely outsources the proof obligation to a human expert. A truly elegant solution will incorporate mechanisms for self-validation, perhaps through the construction of formal proofs of correctness for each coding rule. This demands a departure from treating coding as a purely pattern-recognition task and a return to its roots in logical deduction.

Furthermore, the issue of imbalanced data is not merely a technical hurdle to overcome with clever sampling strategies. It reflects a deeper problem: the tendency to prioritize quantity over quality. A single disconfirming instance is, in principle, more valuable than a thousand confirming ones. Research must grapple with the implications of this asymmetry, and develop methodologies that appropriately weight the significance of rare but critical cases. The aim should not be to ‘handle’ imbalanced data, but to embrace the inherent value of outliers.

Original article: https://arxiv.org/pdf/2512.21041.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/